adaptive neighborhood granularity selection and combination based on margin distribution optimization abstract granular computing aims to develop a granular view for interpreting and solving problems the model of neighborhood rough sets is one of effective tools for granular computing this model can deal with complex tasks of classification learning despite the success of the neighborhood model in attribute reduction and rule learning it still suffers from the issue of granularity selection namely it is an open problem to select a proper granularity of neighborhood for a specific task in this work we explore ensemble learning techniques for adaptively evaluating and combine the models derived from multiple granularity in the proposed framework base classifiers are trained in different granular spaces the importance of base classifiers is then learned by optimizing the margin distribution of the combined system experimental analysis shows that the proposed method can adaptively select a proper granularity and combining the models trained in multi granularity spaces leads to competent performance

feature extraction by non parametric mutual information maximization we present a method for learning discriminative feature transforms using as criterion the mutual information between class labels and transformed features instead of a commonly used mutual information measure based on kullback leibler divergence we use a quadratic divergence measure which allows us to make an efficient non parametric implementation and requires no prior assumptions about class densities in addition to linear transforms we also discuss nonlinear transforms that are implemented as radial basis function networks extensions to reduce the computational complexity are also presented and a comparison to greedy feature selection is made

discriminative least squares regression for multiclass classification and feature selection this paper presents a framework of discriminative least squares regression lsr for multiclass classification and feature selection the core idea is to enlarge the distance between different classes under the conceptual framework of lsr first a technique called e dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged then the e draggings are integrated into the lsr model for multiclass classification our learning framework referred to as discriminative lsr has a compact model form where there is no need to train two class machines that are independent of each other with its compact form this model can be naturally extended for feature selection this goal is achieved in terms of l2 1 norm of matrix generating a sparse learning model for feature selection the model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently experimental evaluation over a range of benchmark datasets indicates the validity of our method

feature combination and the knn framework in object classification in object classification feature combination can usually be used to combine the strength of multiple complementary features and produce better classification results than any single one while multiple kernel learning mkl is a popular approach to feature combination in object classification it does not always perform well in practical applications on one hand the optimization process in mkl usually involves a huge consumption of computation and memory space on the other hand in some cases mkl is found to perform no better than the baseline combination methods this observation motivates us to investigate the underlying mechanism of feature combination with average combination and weighted average combination as a result we empirically find that in average combination it is better to use a sample of the most powerful features instead of all whereas in one type of weighted average combination the best classification accuracy comes from a nearly sparse combination we integrate these observations into the k nearest neighbors knns framework based on which we further discuss some issues related to sparse solution and mkl finally by making use of the knn framework we present a new weighted average combination method which is shown to perform better than mkl in both accuracy and efficiency in experiments we believe that the work in this paper is helpful in exploring the mechanism underlying feature combination

on fuzzy approximation operators in attribute reduction with fuzzy rough sets generally speaking there are four fuzzy approximation operators defined on a general triangular norm t norm framework in fuzzy rough sets different types of t norms specify various approximation operators one issue whether and how the different fuzzy approximation operators affect the result of attribute reduction is then arisen this paper addresses this issue from the theoretical viewpoint by reviewing attribute reduction with fuzzy rough sets and then describing and proving some theorems which demonstrate the effects of the fuzzy approximation operators on the results of attribute reduction first we review some notions of attribute reduction with fuzzy rough sets such as positive region dependency degree and attribute reduction we then present and prove some theorems which describe how and to what degree fuzzy approximation operators impact the performance of attribute reduction finally we report some experimental simulation results which demonstrate the effectiveness and correctness of the theoretical contributions one main contribution in this paper is that we have described and proven that each attribute reduction obtained using one type of fuzzy lower approximation operator always contains one reduction obtained using the other type of fuzzy lower approximation operator

consistency based search in feature selection feature selection is an effective technique in dealing with dimensionality reduction for classification it is used to find an optimal subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved feature selection methods contain two important aspects evaluation of a candidate feature subset and search through the feature space existing algorithms adopt various measures to evaluate the goodness of feature subsets this work focuses on inconsistency measure according to which a feature subset is inconsistent if there exist at least two instances with same feature values but with different class labels we compare inconsistency measure with other measures and study different search strategies such as exhaustive complete heuristic and random search that can be applied to this measure we conduct an empirical study to examine the pros and cons of these search methods give some guidelines on choosing a search method and compare the classifier error rates before and after feature selection

a theory of stochastic systems part ii process algebra this paper introduces pronounce spades a stochastic process algebra for discrete event systems that extends traditional process algebra with timed actions whose delay is governed by general a o continuous probability distributions the operational semantics is defined in terms of stochastic automata a model that uses clocks like in timed automata to symbolically represent randomly timed systems cf the accompanying paper p r d argenio j p katoen a theory of stochastic systems part i stochastic automata inf comput 2005 to appear we show that stochastic automata and are equally expressive and prove that the operational semantics of a term up to conversion of clocks is unique modulo symbolic bisimulation open probabilistic and structural bisimulation are proven to be congruences for and are equipped with an equational theory the equational theory is shown to be complete for structural bisimulation and allows to derive an expansion law

rfrr robust fuzzy rough reduction this paper proposes a robust method of dimension reduction using fuzzy rough sets in which the reduction results can reflect the reducts obtained on all of the possible parameters here the reducts being obtained on all of the possible parameters mean that all of the reducts are obtained on different degrees of robustness to handle noise this method is completely different from the existing methods of fuzzy rough reduction the differences are shown in three aspects the concept the tool and the algorithm first the key concept of attribute reduction is redefined in a new way that is the robust fuzzy rough reduct which is shortened to a robust reduct is proposed to reflect the classical reducts obtained on all of the possible parameters the new robust reduct is not a crisp subset of condition attributes rather it is a fuzzy subset whose most interesting property is that any cut set of the robust reduct is a classical reduct on a certain parameter second the tool used to measure the discernibility power is different from the existing discernibility measures in this paper the robustness of each attribute to handle misclassification and perturbation is considered by considering both the robustness and the discernibility a robust fuzzy discernibility matrix is designed finally the algorithms used to find the robust reducts are designed based upon the robust fuzzy discernibility matrix which is completely different from the existing algorithms used to find the classical reducts

lumping based equivalences in markovian automata algorithms and applications to product form analyses abstract in this paper we consider two relations over stochastic automata named lumpable bisimulation and exact equivalence that induce a strong and an exact lumping respectively on the underlying markov chains we show that an exact equivalence over the states of a non synchronising automaton is indeed a lumpable bisimulation for the corresponding reversed automaton and then it induces a strong lumping on the time reversed markov chain underlying the model this property allows us to prove that the class of quasi reversible models is closed under exact equivalence quasi reversibility is a pivotal property to study product form models hence exact equivalence turns out to be a theoretical tool to prove the product form of models by showing that they are exactly equivalent to models which are known to be quasi reversible algorithms for computing both lumpable bisimulation and exact equivalence are introduced case studies as well as performance tests are also presented

fuzzy probabilistic approximation spaces and their information measures rough set theory has proven to be an efficient tool for modeling and reasoning with uncertainty information by introducing probability into fuzzy approximation space a theory about fuzzy probabilistic approximation spaces is proposed in this paper which combines three types of uncertainty probability fuzziness and roughness into a rough set model we introduce shannon s entropy to measure information quantity implied in a pawlak s approximation space and then present a novel representation of shannon s entropy with a relation matrix based on the modified formulas some generalizations of the entropy are proposed to calculate the information in a fuzzy approximation space and a fuzzy probabilistic approximation space respectively as a result uniform representations of approximation spaces and their information measures are formed with this work

a statistical approach to texture classification from single images we investigate texture classification from single images obtained under unknown viewpoint and illumination a statistical approach is developed where textures are modelled by the joint probability distribution of filter responses this distribution is represented by the frequency histogram of filter response cluster centres textons recognition proceeds from single uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional classification performance is compared with the filter banks and methods of leung and malik ijcv 2001 schmid cvpr 2001 and cula and dana ijcv 2004 and it is demonstrated that superior performance is achieved here classification results are presented for all 61 materials in the columbia utrecht texture database we also discuss the effects of various parameters on our classification algorithm such as the choice of filter bank and rotational invariance the size of the texton dictionary as well as the number of training images used finally we present a method of reliably measuring relative orientation co occurrence statistics in a rotationally invariant manner and discuss whether incorporating such information can enhance the classifier s performance

random sequential adsorption of spheres of different sizes the random sequential adsorption of spheres selected from monodisperse binary and polydisperse distributions on a three dimensional substrate has been studied by computer simulations in the monodisperse case the adsorbed particle density algebraically approaches an asymptotic value of 0 384 0 001 according to feder s law feder j theor biol 87 1980 237 for the case of spheres of two different sizes the large sphere density approaches its asymptotic value exponentially while the small sphere density increases according to feder s law if the sphere radii ri are uniformly distributed over the range ra ri rb before testing for overlaps then the approach to the asymptotic density is algebraic but the effective exponent depends on the model parameter r rb ra

a simple feature combination method based on dominant sets abstract feature combination is a popular method for improving object classification performances in this paper we present a simple and effective weighting scheme for feature combination based on the dominant set notion of a cluster specifically we use dominant sets clustering to evaluate how accurate a kernel matrix is expected to be for a svm classifier this expected kernel accuracy reflects the discriminative power of the kernel matrix and thus used in weighting the kernel matrix in feature combination our method is simple intuitive memory and computation efficient and performs comparably to the popular and sophisticated optimization based methods we conduct experiments with several datasets of diverse object types and validate the effectiveness of the proposed method in fact in five out of the six datasets used in our experiments we obtained the best results until now in our knowledge

space structure and clustering of categorical data learning from categorical data plays a fundamental role in such areas as pattern recognition machine learning data mining and knowledge discovery to effectively discover the group structure inherent in a set of categorical objects many categorical clustering algorithms have been developed in the literature among which k modes type algorithms are very representative because of their good performance nevertheless there is still much room for improving their clustering performance in comparison with the clustering algorithms for the numeric data this may arise from the fact that the categorical data lack a clear space structure as that of the numeric data to address this issue we propose in this paper a novel data representation scheme for the categorical data which maps a set of categorical objects into a euclidean space based on the data representation scheme a general framework for space structure based categorical clustering algorithms sbc is designed this framework together with the applications of two kinds of dissimilarities leads two versions of the sbc type algorithms to verify the performance of the sbc type algorithms we employ as references four representative algorithms of the k modes type algorithms experiments show that the proposed sbc type algorithms significantly outperform the k modes type algorithms

relational interpretations of neighborhood operators and rough set approximation operators this paper presents a framework for the formulation interpretation and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations a special class of neighborhood systems called 1 neighborhood systems is introduced three extensions of pawlak approximation operators are analyzed properties of neighborhood and approximation operators are studied and their connections are examined

geometry of random sequential adsorption by sequentially adding line segments to a line or disks to a surface at random positions without overlaps we obtain configurations of the one and two dimensional random sequential adsorption rsa problem we have simulated the one and two dimensional problem with periodic boundary condition the one dimensional simulations are compared with the exact analytical solutions to give an estimate of the accuracy of the simulation in two dimensions the geometrical properties of the rsa configuration are discussed and in addition known results of the rsa process are reproduced various statistical distributions of the voronoi dirichlet vd network corresponding to the rsa disk configuration are analyzed in order to characterize pores in the rsa configuration we introduce circular holes there is a direct correspondence between vertices of the vd network and these holes and also between direct indirect geometrical neighbors and these holes the hole size distribution is found to be a parabola we also find general relations that connect the asymptotic behavior of the surface coverage the correlation function and the hole size distribution

reactive generative and stratified models of probabilistic processes abstract we introduce three models of probabilistic processes namely reactive generative and stratified these models are investigated within the context of pccs an extension of milner s sccs in which each summand of a process summation expression is guarded by a probability and the sum of these probabilities is 1 for each model we present a structural operational semantics of pccs and a notion of bisimulation equivalence which we prove to be a congruence we also show that the models form a hierarchy the reactive model is derivable from the generative model by abstraction from the relative probabilities of different actions and the generative model is derivable from the stratified model by abstraction from the purely probabilistic branching structure moreover the classical nonprobabilistic model is derivable from each of these models by abstraction from all probabilities

precise algorithm to generate random sequential adsorption of hard polygons at saturation random sequential adsorption rsa is a time dependent packing process in which particles of certain shapes are randomly and sequentially placed into an empty space without overlap in the infinite time limit the density approaches a saturation limit although this limit has attracted particular research interest the majority of past studies could only probe this limit by extrapolation we have previously found an algorithm to reach this limit using finite computational time for spherical particles and could thus determine the saturation density of spheres with high accuracy in this paper we generalize this algorithm to generate saturated rsa packings of two dimensional polygons we also calculate the saturation density for regular polygons of three to ten sides and obtain results that are consistent with previous extrapolation based studies

random sequential adsorption of polydisperse mixtures asymptotic kinetics and structure the authors study the asymptotic kinetics of the random sequential adsorption rsa of a mixture of particles with a continuous distribution of sizes their results provide further support for the idea that the power law exponent for the approach to the jamming limit is simply related to the number of degrees of freedom of the adsorbing species they also predict the behaviour of the mean radial distribution function in the asymptotic regime

an overlapping detection algorithm for random sequential packing of elliptical particles abstract random sequential packing of particles is a subject of intense research in many branches of physics and engineering the preponderance of preview work has focused on spherical particles and little is known about non overlapping elliptical particles in the present work a new numerical algorithm is developed to detect overlapping of ellipses by a series of sequential coordinate transformations and a novel golden section search algorithm the accuracy and efficiency of the numerical algorithm are tested and compared with algorithms developed in previous literature its stability is verified by visualizations of random sequential packing of monodispersed and polydispersed ellipses with different boundary conditions then the algorithm is applied to investigate the influence of the shape of ellipses on the random packing fraction as well as to study the influence of the shape and size of ellipses on the wall effect in the particle packing structure and some numerical results are demonstrated finally the reliability of the numerical algorithm extended to the three dimensional space is also evaluated by checking overlapping of ellipsoidal particles

saturation coverage in random sequential adsorption of very elongated particles abstract in the random sequential adsorption rsa of unoriented anisotropic objects onto a flat uniform surface the saturation coverage goes to zero when the aspect ratio of the objects becomes infinite by scaling arguments we show that follows a power law p where p 1 1 2 2 the fractal dimension of the system of adsorbed needles is also discussed

random sequential adsorption of lines and ellipses infinitely many lines of zero thickness may be adsorbed onto a plane without overlap and it is shown that the number of lines adsorbed should scale as 13 where is the number of adsorption trials per unit area of surface this is confirmed by numerical simulation the adsorption of ellipses with major and minor axes 2a 2b respectively is also studied the area coverage theta approaches its maximum value theta max as alpha as to infinity where alpha is typically 1 3 as b a decreases from unity theta max first increases to a maximum value 0 58 or 0 01 when b approximately 0 5 and then decreases a feature not noted in previous work

a tutorial on empa a theory of concurrent processes with nondeterminism priorities probabilities and time we give an overview of empa a process algebra for modeling and analyzing concurrent systems where nondeterminism priorities probabilities and time are present aiming at a reasonable trade off between the expressive power of the calculus and the complexity of its underlying theory

complexity measures of supervised classification problems we studied a number of measures that characterize the difficulty of a classification problem focusing on the geometrical complexity of the class boundary we compared a set of real world problems to random labelings of points and found that real problems contain structures in this measurement space that are significantly different from the random sets distributions of problems in this space show that there exist at least two independent factors affecting a problem s difficulty we suggest using this space to describe a classifier s domain of competence this can guide static and dynamic selection of classifiers for specific problems as well as subproblems formed by confinement projection and transformations of the feature vectors

decentralized dimensionality reduction for distributed tensor data across sensor networks this paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks the main contributions of this paper are as follows first conventional centralized methods which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode are not suitable for the network environment here we relax the simultaneous processing manner into the one vector by one vector ovbov manner i e determining the projection vectors pvs related to each tensor mode one by one second we prove that in the ovbov manner each pv can be determined without modifying any tensor data which simplifies corresponding computations third we cast the decentralized pv determination problem as a set of subproblems with consensus constraints so that it can be solved in the network environment only by local computations and information communications among neighboring nodes fourth we introduce the null space and transform the pv determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint which can be solved by the lagrange multiplier method finally experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks

granular matrix a new approach for granular structure reduction and redundancy evaluation granular structure is a mathematical expression of knowledge in granular computing and a direct determinant of the data processing efficiency to improve the efficiency of data processing many scholars have studied the reduction of granular structure the attribute reduction and the granular reduction are two types of reduction on different layers of a granular structure with the latter being both an essential step for granular structure reduction and the foundation of the attribute reduction yet compared with the attribute reduction the granular reduction has received less attention from scholars therefore a fuzzy granular reduction theory and a granular matrix based on the fuzzy beta coverings is proposed in this article the insufficiency of the existing granular reduction theory for fuzzy beta coverings is pointed out and proper sufficient and necessary conditions for two fuzzy beta coverings generating the same upper and lower approximations are also given in this article in addition to reduce and evaluate a fuzzy beta covering a novel reduction algorithm based on a granular matrix is proposed for the first time also since fuzzy covering reduction is np hard a heuristic greedy algorithm is designed to obtain a reduct numerical experiments show that the redundancy rates of neighborhood granule sets induced by some big scale data sets exceed 99 which indicates that the existing neighborhood granulation methods need to be urgently improved based on this concise granular structures and much more efficient feature selection algorithms can be proposed in the future

a general conservative extension theorem in process algebras with inequalities we prove a general conservative extension theorem for transition system based process theories with easy to check and reasonable conditions the core of this result is another general theorem which gives sufficient conditions for a system of operational rules and an extension of it in order to ensure conservativity that is provable transitions from an original term in the extension are the same as in the original system as a simple corollary of the conservative extension theorem we prove a completeness theorem we also prove a general theorem giving sufficient conditions to reduce the question of ground confluence modulo some equations for a large term rewriting system associated with an equational process theory to a small term rewriting system under the condition that the large system is a conservative extension of the small one we provide many applications to show that our results are useful the applications include but are not limited to various real and discrete time settings in acp atp and ccs and the notions projection renaming stage operator priority recursion the silent step autonomous actions the empty process divergence etc

constraint markov chains notions of specification implementation satisfaction and refinement together with operators supporting stepwise design constitute a specification theory we construct such a theory for markov chains mcs employing a new abstraction of a constraint mc constraint mcs permit rich constraints on probability distributions and thus generalize prior abstractions such as interval mcs linear polynomial constraints suffice for closure under conjunction respectively parallel composition this is the first specification theory for mcs with such closure properties we discuss its relation to simpler operators for known languages such as probabilistic process algebra despite the generality all operators and relations are computable

uncertainly measures of rough set prediction abstract the main statistics used in rough set data analysis the approximation quality is of limited value when there is a choice of competing models for predicting a decision variable in keeping within the rough set philosophy of non invasive data analysis we present three model selection criteria using information theoretic entropy in the spirit of the minimum description length principle our main procedure is based on the principle of indifference combined with the maximum entropy principle thus keeping external model assumptions to a minimum the applicability of the proposed method is demonstrated by a comparison of its error rates with results of c4 5 using 14 published data sets

unsupervised feature selection using feature similarity in this article we describe an unsupervised feature selection algorithm suitable for data sets large in both dimension and size the method is based on measuring similarity between features whereby redundancy therein is removed this does not need any search and therefore is fast a new feature similarity measure called maximum information compression index is introduced the algorithm is generic in nature and has the capability of multiscale representation of data sets the superiority of the algorithm in terms of speed and performance is established extensively over various real life data sets of different sizes and dimensions it is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure

data classification based on tolerant rough set abstract this paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value the determination of optimal similarity threshold value is very important for the accurate classification so we determine it optimally by using the genetic algorithm ga where the goal of evolution is to balance two requirements such that 1 some tolerant objects are required to be included in the same class as many as possible and 2 some objects in the same class are required to be tolerable as much as possible after finding the optimal similarity threshold value a tolerant set of each object is obtained and the data set is grouped into the lower and upper approximation set depending on the coincidence of their classes we propose a two stage classification method that all data are classified by using the lower approximation at the first stage and then the non classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set the validity of the proposed classification method is tested by applying it to the iris data classification and its classification performance and processing time are compared with those of other classification methods such as bpnn ofunn and fcm

input feature selection by mutual information based on parzen window mutual information is a good indicator of relevance between variables and have been used as a measure in several feature selection algorithms however calculating the mutual information is difficult and the performance of a feature selection algorithm depends on the accuracy of the mutual information in this paper we propose a new method of calculating mutual information between input and class variables based on the parzen window and we apply this to a feature selection algorithm for classification problems

nearest neighbors by neighborhood counting finding nearest neighbors is a general idea that underlies many artificial intelligence tasks including machine learning data mining natural language understanding and information retrieval this idea is explicitly used in the k nearest neighbors algorithm knn a popular classification method in this paper this idea is adopted in the development of a general methodology neighborhood counting for devising similarity functions we turn our focus from neighbors to neighborhoods a region in the data space covering the data point in question to measure the similarity between two data points we consider all neighborhoods that cover both data points we propose to use the number of such neighborhoods as a measure of similarity neighborhood can be defined for different types of data in different ways here we consider one definition of neighborhood for multivariate data and derive a formula for such similarity called neighborhood counting measure or ncm ncm was tested experimentally in the framework of knn experiments show that ncm is generally comparable to vdm and its variants the state of the art distance functions for multivariate data and at the same time is consistently better for relatively large k values additionally ncm consistently outperforms heom a mixture of euclidean and hamming distances the standard and most widely used distance function for multivariate data ncm has a computational complexity in the same order as the standard euclidean distance function and ncm is task independent and works for numerical and categorical data in a conceptually uniform way the neighborhood counting methodology is proven sound for multivariate data experimentally we hope it works for other types of data

effective discriminative feature selection with nontrivial solution feature selection and feature transformation the two main ways to reduce dimensionality are often presented separately in this paper a feature selection method is proposed by combining the popular transformation based dimensionality reduction method linear discriminant analysis lda and sparsity regularization we impose row sparsity on the transformation matrix of lda through ell 2 1 norm regularization to achieve feature selection and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously the formulation is extended to the ell 2 p norm regularized case which is more likely to offer better sparsity when 0 thus the formulation is a better approximation to the feature selection problem an efficient algorithm is developed to solve the ell 2 p norm based optimization problem and it is proved that the algorithm converges when 0 systematical experiments are conducted to understand the work of the proposed method promising experimental results on various types of real world data sets demonstrate the effectiveness of our algorithm

rough set models in multigranulation spaces there exist several approaches to rough set approximations in a multigranulation space namely a family of equivalence relations in this paper we propose a unified framework to classify and compare existing studies an underlying principle is to explain rough sets in a multigranulation space through rough sets derived by using individual equivalence relations two basic models are suggested one model is based on a combination of a family of equivalence relations into an equivalence relation and the construction of approximations with respect to the combined relation by combining equivalence relations through set intersection and union respectively we construct two sub models the other model is based on the construction of a family of approximations from a set of equivalence relations and a combination of the family of approximations by using set intersection and union to combine a family of approximations respectively we again build two sub models as a result we have a total of four models we examine these models and give conditions under which some of them become the same

neighborhood rough set based heterogeneous feature subset selection feature subset selection is viewed as an important preprocessing step for pattern recognition machine learning and data mining most of researches are focused on dealing with homogeneous feature selection namely numerical or categorical features in this paper we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection as the classical rough set model can just be used to evaluate categorical features we generalize this model with neighborhood relations and introduce a neighborhood rough set model the proposed model will degrade to the classical one if we specify the size of neighborhood zero the neighborhood model is used to reduce numerical and categorical features by assigning different thresholds for different kinds of attributes in this model the sizes of the neighborhood lower and upper approximations of decisions reflect the discriminating capability of feature subsets the size of lower approximation is computed as the dependency between decision and condition attributes we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous features and construct forward feature subset selection algorithms the proposed algorithms are compared with some classical techniques experimental results show that the neighborhood model based method is more flexible to deal with heterogeneous data

a reformulation of entropy in the presence of indistinguishability operators this paper deals with the measurement of entropy when an indistinguishability relation on the set of events has been defined our approach states that entropy could be measured in terms of the observed distinguishability of the set of events in this sense the observer paradigm is introduced and definitions for joint and conditional entropy under this paradigm are given we also present some interesting properties and relationships with shannon s entropy measure

feature selection based on neighborhood discrimination index feature selection is viewed as an important preprocessing step for pattern recognition machine learning and data mining neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions in this paper a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation it reflects the distinguishing ability of a feature subset the proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes variants of the discrimination index including joint discrimination index conditional discrimination index and mutual discrimination index are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets they have the similar properties as shannon entropy and its variants a parameter named neighborhood radius is introduced in these discrimination measures to address the analysis of real valued data based on the proposed discrimination measures the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms the experimental results confirm that the discrimination index based algorithm yields superior performance compared to other classical algorithms

kinetics and fractal properties of the random sequential adsorption of line segments the random sequential adsorption rsa of infinitely thin line segments is studied by computer simulation it is shown that the exponent for the time dependence of the surface coverage increases beyond the value 0 33 reported by sherwood 1990 to a value of 0 38 at later times it is also argued that the fractal dimension of the adsorbed lines is about 1 8 making use of a previous result on the rsa of rectangular objects

composition and behaviors of probabilistic i o automata abstract we augment the i o automaton model of lynch and tuttle with probability as a step toward the ultimate goal of obtaining a useful tool for specifying and reasoning about asynchronous probabilistic systems our new model called probabilistic i o automata preserves the fundamental properties of the i o automaton model such as the asymmetric treatment of input and output and the pleasant notion of asynchronous composition for certain classes of probabilistic i o automata we show that probabilistic behavior maps which are an abstract representation of i o automaton behavior in terms of a certain expectation operator are compositional and fully abstract with respect to a natural notion of probabilistic testing

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

process algebra for performance evaluation this paper surveys the theoretical developments in the field of stochastic process algebras process algebras where action occurrences may be subject to a delay that is determined by a random variable a huge class of resource sharing systems like large scale computers client server architectures networks can accurately be described using such stochastic specification formalisms the main emphasis of this paper is the treatment of operational semantics notions of equivalence and sound and complete axiomatisations of these equivalences for different types of markovian process algebras where delays are governed by exponential distributions starting from a simple actionless algebra for describing time homogeneous continuous time markov chains we consider the integration of actions and random delays both as a single entity like in known markovian process algebras like tipp pepa and empa and as separate entities like in the timed process algebras timed csp and tccs in total we consider four related calculi and investigate their relationship to existing markovian process algebras we also briefly indicate how one can profit from the separation of time and actions when incorporating more general non markovian distributions

universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning universal blind image quality assessment iqa metrics that can work for various distortions are of great importance for image processing systems because neither ground truths are available nor the distortion types are aware all the time in practice existing state of the art universal blind iqa algorithms are developed based on natural scene statistics nss although nss based metrics obtained promising performance they have some limitations 1 they use either the gaussian scale mixture model or generalized gaussian density to predict the nongaussian marginal distribution of wavelet gabor or discrete cosine transform coefficients the prediction error makes the extracted features unable to reflect the change in nongaussianity ng accurately the existing algorithms use the joint statistical model and structural similarity to model the local dependency ld although this ld essentially encodes the information redundancy in natural images these models do not use information divergence to measure the ld although the exponential decay characteristic edc represents the property of natural images that large small wavelet coefficient magnitudes tend to be persistent across scales which is highly correlated with image degradations it has not been applied to the universal blind iqa metrics and 2 all the universal blind iqa metrics use the same similarity measure for different features for learning the universal blind iqa metrics though these features have different properties to address the aforementioned problems we propose to construct new universal blind quality indicators using all the three types of nss i e the ng ld and edc and incorporating the heterogeneous property of multiple kernel learning mkl by analyzing how different distortions affect these statistical properties we present two universal blind quality assessment models nss global scheme and nss two step scheme in the proposed metrics 1 we exploit the ng of natural images using the original marginal distribution of wavelet coefficients 2 we measure correlations between wavelet coefficients using mutual information defined in information theory 3 we use features of edc in universal blind image quality prediction directly and 4 we introduce mkl to measure the similarity of different features using different kernels thorough experimental results on the laboratory for image and video engineering database ii and the tampere image database2008 demonstrate that both metrics are in remarkably high consistency with the human perception and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes for various types of distortions

packing of spheroids in three dimensional space by random sequential addition packings of spheroidal particles with semi axes of length a b b were generated by random sequential addition rsa simulations as the jamming limit is approached the volume fraction occupied by particles tends towards an asymptote which was determined as a function of aspect ratio this asymptote has a local minimum at spheres with local maxima at prolate spheroids and oblate values of agree with results from rsa simulations of sphere packings but lie below volume fractions obtained in simulations of near spheres packed under gravity volume fractions reported for simulations of spheroids packed under gravity vary widely when the aspect ratio is very large or small differences between these results and the predictions of rsa are discussed

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

ssc a classifier combination method based on signal strength we propose a new classifier combination method the signal strength based combining ssc approach to combine the outputs of multiple classifiers to support the decision making process in classification tasks as ensemble learning methods have attracted growing attention from both academia and industry recently it is critical to understand the fundamental issues of the combining rule motivated by the signal strength concept our proposed ssc algorithm can effectively integrate the individual vote from different classifiers in an ensemble learning system comparative studies of our method with nine major existing combining rules namely geometric average rule arithmetic average rule median value rule majority voting rule borda count max and min rule weighted average and weighted majority voting rules is presented furthermore we also discuss the relationship of the proposed method with respect to margin based classifiers including the boosting method adaboost m1 and adaboost m2 and support vector machines by margin analysis detailed analyses of margin distribution graphs are presented to discuss the characteristics of the proposed method simulation results for various real world datasets illustrate the effectiveness of the proposed method

a fitting model for feature selection with fuzzy rough sets a fuzzy rough set is an important rough set model used for feature selection it uses the fuzzy rough dependency as a criterion for feature selection however this model can merely maintain a maximal dependency function it does not fit a given dataset well and cannot ideally describe the differences in sample classification therefore in this study we introduce a new model for handling this problem first we define the fuzzy decision of a sample using the concept of fuzzy neighborhood then a parameterized fuzzy relation is introduced to characterize the fuzzy information granules using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced this can guarantee that the membership degree of a sample to its own category reaches the maximal value furthermore this approach can fit a given dataset and effectively prevents samples from being misclassified finally we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets especially for those datasets for which different categories exhibit a large degree of overlap

information theoretic measures of uncertainty for rough sets and rough relational databases rough set theory has become well established as a mechanism for uncertainty management in a wide variety of applications including databases this paper addresses the measurement of uncertainty in rough sets and rough relational databases by introducing a measurement based on information theory this rough entropy is discussed as it applies to rough sets in general and in particular to aspects of the rough relational database model

a novel algorithm for finding reducts with fuzzy rough sets attribute reduction is one of the most meaningful research topics in the existing fuzzy rough sets and the approach of discernibility matrix is the mathematical foundation of computing reducts when computing reducts with discernibility matrix we find that only the minimal elements in a discernibility matrix are sufficient and necessary this fact motivates our idea in this paper to develop a novel algorithm to find reducts that are based on the minimal elements in the discernibility matrix relative discernibility relations of conditional attributes are defined and minimal elements in the fuzzy discernibility matrix are characterized by the relative discernibility relations then the algorithms to compute minimal elements and reducts are developed in the framework of fuzzy rough sets experimental comparison shows that the proposed algorithms are effective

random sequential adsorption line segments on the square lattice we study kinetics of the rmgle layer random sequenrisl adsorption of lint segments of ked kn5h on the square iatijcc by a monte carlo srmulatton the area covered b the line segments grows with time and finally reacher a jamming limit when no more adsorption is possible the jamming coverage depends on the segment length and 11s vanation is studted at the late stage approach of the coverage to the jamming limit is arymptotieally exponential with a rate found to be independent of the segment length based on our monte carlo data an act expression for the late stage deposition kinettcs 15 conjectured random sequential adsorption rsa has been the subject of much recent experimental and theoretical investigation objects of figte size are randomly deposited adsorbed on an initially empty substrate so that no two objects overlap the quantity of interest is the fraction of the total area b f in time i covered by the depositing particles due to the blocking of the area by the already randomly adsorbed particles the limiting 3amming coverage co is less than the close packing the emergence of this jammed state is iduenced by the infiuite memory effects consequently its formation cannot he described by mean field theory except for very early times when 6 t af experimental studies 11 41 include e g adhesion of colloidal particles 131 and proteins 2 on homogeneous substrates with relaxation times much longer than the formation time of deposits theoretical studies s 171 of rsa include series expansions is l j numerical monte carlo mc simulations 9 14 16 and some analytical results 1171 mostly for one dimensional systems in such studies it was shown that the precise form of the long time behaviour of b r depends on the shape and orientational freedom of the adsorbing particles earlier studies have focused on continuum deposition models for which a power law behaviour of the late stage deposition 1171

neighborhood operator systems and approximations this paper presents a framework for the study of generalizing the standard notion of equivalence relation in rough set approximation space with various categories of k step neighborhood systems based on a binary relation on a finite universe six families of binary relations are obtained and the corresponding six classes of k step neighborhood systems are derived extensions of pawlak s rough set approximation operators based on such neighborhood systems are proposed properties of neighborhood operator systems and rough set approximation operators are investigated and their connections are examined

bisimulation through probabilistic testing abstract we propose a language for testing concurrent processes and examine its strength in terms of the processes that are distinguished by a test by using probabilistic transition systems as the underlying semantic model we show how a testing algorithm can distinguish with a probability arbitrarily close to one between processes that are not bisimulation equivalent we also show a similar result in a slightly stronger form for a new process relation called 2 3 bisimulation which lies strictly between that of simulation and bisimulation finally the ultimately strength of the testing language is shown to identify a new process relation called probabilistic bisimulation which is strictly stronger than bisimulation

uncertainty measures for general fuzzy relations abstract relation is one of the important concepts in rough set models some uncertainty measures for relations were proposed in recent years by generalizing information entropy however the existing theories considered only special relations and do not address mixed type data well they always transformed each attribute into a binary relation with the same structure regardless of attribute types as a result the structure information can be lost for other types of attributes in this paper a new theory about uncertainty measures for general fuzzy relations is proposed the proposed theory can overcome the weakness of the existing theories we first introduce a novel entropy to compute the uncertainty information of fuzzy binary relations and then present the concepts of joint fuzzy entropy conditional fuzzy entropy and fuzzy mutual information the basic properties of these uncertainty measures are studied finally we apply the proposed theory to perform attribute reduction of heterogeneous data sets the experimental results show that our proposed method is feasible and effective in some cases the proposed theory can provide a fundamental framework that integrates all kinds of methods for the uncertainty information of special relations

complex valued filtering based on the minimization of complex error entropy in this paper we consider the training of complex valued filter based on the information theoretic method we first generalize the error entropy criterion to complex domain to present the complex error entropy criterion ceec due to the difficulty in estimating the entropy of complex valued error directly the entropy bound minimization ebm method is used to compute the upper bounds of the entropy of the complex valued error and the tightest bound selected by the ebm algorithm is used as the estimator of the complex error entropy then based on the minimization of complex error entropy mcee and the complex gradient descent approach complex valued learning algorithms for both the linear transverse filter and the nonlinear neural network are derived the algorithms are applied to complex valued linear filtering and complex valued nonlinear channel equalization to demonstrate their effectiveness and advantages

structural operational semantics for stochastic and weighted transition systems we introduce weighted gsos a general syntactic framework to specify well behaved transition systems where transitions are equipped with weights coming from a commutative monoid we prove that weighted bisimilarity is a congruence on systems defined by weighted gsos specifications we illustrate the flexibility of the framework by instantiating it to handle some special cases most notably that of stochastic transition systems through examples we provide weighted gsos definitions for common stochastic operators in the literature

branching time and abstraction in bisimulation semantics in comparative concurrency semantics one usually distinguishes between linear time and branching time semantic equivalences milner s notion of observatin equivalence is often mentioned as the standard example of a branching time equivalence in this paper we investigate whether observation equivalence really does respect the branching structure of processes and find that in the presence of the unobservable action of ccs this is not the case therefore the notion of branching bisimulation equivalence is introduced which strongly preserves the branching structure of processes in the sense that it preserves computations together with the potentials in all intermediate states that are passed through even if silent moves are involved on closed ccs terms branching bisimulation congruence can be completely axiomatized by the single axion scheme a y z y a y z where a ranges over all actions and the usual loaws for strong congruence we also establish that for sequential processes observation equivalence is not preserved under refinement of actions whereas branching bisimulation is for a large class of processes it turns out that branching bisimulation and observation equivalence are the same as far as we know all protocols that have been verified in the setting of observation equivalence happen to fit in this class and hence are also valid in the stronger setting of branching bisimulation equivalence

learning the kernel matrix with semidefinite programming kernel based learning algorithms work by embedding the data into a euclidean space and then searching for linear relations among the embedded data points the embedding is performed implicitly by specifying the inner products between each pair of points in the embedding space this information is contained in the so called kernel matrix a symmetric and positive semidefinite matrix that encodes the relative positions of all points specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space classical model selection problems in machine learning in this paper we show how the kernel matrix can be learned from data via semidefinite programming sdp techniques when applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm using the labeled part of the data one can learn an embedding also for the unlabeled part the similarity between test points is inferred from training points and their labels importantly these learning problems are convex so we obtain a method for learning both the model class and the function without local minima furthermore this approach leads directly to a convex method for learning the 2 norm soft margin parameter in support vector machines solving an important open problem

generalized fuzzy rough sets determined by a triangular norm the theory of rough sets has become well established as an approach for uncertainty management in a wide variety of applications various fuzzy generalizations of rough approximations have been made over the years this paper presents a general framework for the study of t fuzzy rough approximation operators in which both the constructive and axiomatic approaches are used by using a pair of dual triangular norms in the constructive approach some definitions of the upper and lower approximation operators of fuzzy sets are proposed and analyzed by means of arbitrary fuzzy relations the connections between special fuzzy relations and the t upper and t lower approximation operators of fuzzy sets are also examined in the axiomatic approach an operator oriented characterization of rough sets is proposed that is t fuzzy approximation operators are defined by axioms different axiom sets of t upper and t lower fuzzy set theoretic operators guarantee the existence of different types of fuzzy relations producing the same operators the independence of axioms characterizing the t fuzzy rough approximation operators is examined then the minimal sets of axioms for the characterization of the t fuzzy approximation operators are presented based on information theory the entropy of the generalized fuzzy approximation space which is similar to shannon s entropy is formulated to measure uncertainty in t generalized fuzzy rough sets a notion of fuzziness is introduced some basic properties of this measure are examined for a special triangular norm t min it is proved that the measure of fuzziness of the generalized fuzzy rough set is equal to zero if and only if the set is crisp and definable

density dependent quantized least squares support vector machine for large data sets based on the knowledge that input data distribution is important for learning a data density dependent quantization scheme dqs is proposed for sparse input data representation the usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well known least squares support vector machine ls svm for application on big data sets essentially the proposed dqs adopts a single shrinkage threshold to obtain a simple quantization scheme which adapts its outputs to input data density with this quantization scheme a large data set is quantized to a small subset where considerable sample size reduction is generally obtained in particular the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the nystrom method based on the quantized subset the approximated features are incorporated into ls svm to develop a data density dependent quantized ls svm dqls svm where an analytic solution is obtained in the primal solution space the developed dqls svm is evaluated on synthetic and benchmark data with particular emphasis on large data sets extensive experimental results show that the learning machine incorporating dqs attains not only high computational efficiency but also good generalization performance

the pair correlation function in a randomly sequentially filled one dimensional lattice we study random sequential adsorption of particles on a one dimensional lattice with exclusion of nearest neighbor occupation the fraction of filled sites in the saturated state y 12 1 e 2 was obtained long ago by widom we show that in the saturated state the probability p k of having two sites a distance k apart both occupied is given by p k y 2 12 1 ke 2 p 0 2k 2p 1 k 2p 1 and hence decays faster than exponentially as k

fuzzy rough sets assisted attribute selection attribute selection as refers to the problem of selecting those input attributes or features that are most predictive of a given outcome a problem encountered in many areas such as machine learning pattern recognition and signal processing unlike other dimensionality reduction methods attribute selectors preserve the original meaning of the attributes after reduction this has found application in tasks that involve datasets containing huge numbers of attributes in the order of tens of thousands which for some learning algorithms might be impossible to process further recent examples include text processing and web content classification as techniques have also been applied to small and medium sized datasets in order to locate the most informative attributes for later use one of the many successful applications of rough set theory has been to this area the rough set ideology of using only the supplied data and no other information has many benefits in as where most other methods require supplementary knowledge however the main limitation of rough set based attribute selection in the literature is the restrictive requirement that all data is discrete in classical rough set theory it is not possible to consider real valued or noisy data this paper investigates a novel approach based on fuzzy rough sets fuzzy rough feature selection frfs that addresses these problems and retains dataset semantics frfs is applied to two challenging domains where a feature reducing step is important namely web content classification and complex systems monitoring the utility of this approach is demonstrated and is compared empirically with several dimensionality reducers in the experimental studies frfs is shown to equal or improve classification accuracy when compared to the results from unreduced data classifiers that use a lower dimensional set of attributes which are retained by fuzzy rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method in addition it is shown that frfs is more powerful than the other as techniques in the comparative study

non trivial exponents in the zero temperature dynamics of the 1d ising and potts models we consider the glauber dynamics of the q state potts model in one dimension at zero temperature starting with a random initial configuration we measure the density r t of spins which have never flipped from the beginning of the simulation until time t we find that for large t the density r t has a power law decay left r t sim t theta right where the exponent theta varies with q our simulations lead to theta simeq 37 for q 2 theta simeq 53 for q 3 and theta longrightarrow 1 as q longrightarrow infty

soft margin multiple kernel learning multiple kernel learning mkl has been proposed for kernel methods by learning the optimal kernel from a set of predefined base kernels however the traditional l 1 rm mkl method often achieves worse results than the simplest method using the average of base kernels i e average kernel in some practical applications in order to improve the effectiveness of mkl this paper presents a novel soft margin perspective for mkl specifically we introduce an additional slack variable called kernel slack variable to each quadratic constraint of mkl which corresponds to one support vector machine model using a single base kernel we first show that l 1 rm mkl can be deemed as hard margin mkl and then we propose a novel soft margin framework for mkl three commonly used loss functions including the hinge loss the square hinge loss and the square loss can be readily incorporated into this framework leading to the new soft margin mkl objective functions many existing mkl methods can be shown as special cases under our soft margin framework for example the hinge loss soft margin mkl leads to a new box constraint for kernel combination coefficients using different hyper parameter values for this formulation we can inherently bridge the method using average kernel l 1 rm mkl and the hinge loss soft margin mkl the square hinge loss soft margin mkl unifies the family of elastic net constraint regularizer based approaches and the square loss soft margin mkl incorporates l 2 rm mkl naturally moreover we also develop efficient algorithms for solving both the hinge loss and square hinge loss soft margin mkl comprehensive experimental studies for various mkl algorithms on several benchmark data sets and two real world applications including video action recognition and event recognition demonstrate that our proposed algorithms can efficiently achieve an effective yet sparse solution for mkl

approaches to knowledge reduction based on variable precision rough set model this paper deals with approaches to knowledge reduction based on variable precision rough set model the concepts of lower distribution reduct and upper distribution reduct based on variable precision rough sets vprs are first introduced their equivalent definitions are then given and the relationships among lower and upper distribution reducts and alternative types of knowledge reduction in inconsistent systems are investigated it is proved that for some special thresholds lower distribution reduct is equivalent to the maximum distribution reduct whereas upper distribution reduct is equivalent to the possible reduct the judgement theorems and discernibility matrices associated with the lower and upper distribution reducts are also established from which we can obtain the approaches to knowledge reduction in vprs