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Hybrid Intelligent Systems offer many alternatives for unorthodox handling of realistic increasingly complex problems, involving ambiguity, uncertainty and high-dimensionality of data.
their combination. The famous ”no free lunch” theorem  stated by Wolpert may be extrapolated to the point of saying that there is no single computational view that solves all problems.
blending them into one effective working system.
new properties that may allow to solve better or more efficiently the proposed problem.
Information sources can be the result of additional computational processes.
Figure 1: Domains of Hybrid Intelligent Systems.
defined weights. In 1979 Dasarathy and Sheela combined a linear classifier and one k-NN classifier , suggesting to identify the region of the feature space where the classifiers disagree.
independent voters ], providing the first result measuring the quality of classifier committee.
the center to the Web of Knowledge.Figure 2: Evolution of the number of publications per year ranges retrieved from the keywords specified in the plot legend. 3 . The last entry is only for the last two years. the bottom to Scopus. The first entry of the plots is for publications prior to 1990. Each plot corresponds to searching site: the top to Google Scholar.
The growth in the number of publications has an exponential trend. though accurate. Nowadays MCS. The figure reproduces the evolution of the number of references retrieved by the application of specific keywords related to MCS since 1990. the combined algorithm.  with majority voting applied to handwriting recognition. such as  who considered a neural network ensemble. Other early relevant works formulated conclusions regarding MCS ’s classification quality. Thus. MCS can exploit bootstrapping methods. by individual classifier output averaging . in the (Probably Approximately Correct) theory  sense. Finally. choosing the search direction according to an heuristic attribute evaluation function. there is widespread acknowledgment of the following advantages of MCS: • MCS behave well in the two extreme cases of data availability: when we have very scarce data samples for learning. but combinations of hypotheses may expand the space of representable functions. such as Borda count. second. however the same idea was developed independently in 1981 by by Rastrigin and Erenstein  performing first a feature space partitioning and. Ho  underlined that a decision combination function must receive useful representation of each classifier’s decision. and when we have a huge amount of them at our disposal. The last entry of the plots corresponds to the last two years. which 4 . Under some conditions (e. on the basis of the weak one. Such an approach does not assure an optimal solution. The experiment was repeated on three well known academic search sites. Seni and Edler . In the event of availability of a huge amount of learning data samples. such as bagging or boosting. majority voting by a group of independent classifiers) this improvement has been proven analytically . the landmark works devoted introducing bagging  and boosting [13. Dietterich  summarized the benefits of MCS: (a) allowing to filter out hypothesis that. Turner in 1996  showed that averaging outputs of an infinite number of unbiased and independent classifiers can lead to the same response as the optimal Bayes classifier. This was the first work introducing a classifier selection concept.. These reviews include the books by Kuncheva . For example the popular decision tree induction method C4. and Baruque and Corchado .5  uses a greedy search approach. they considered several method based on decision ranks. Rokach . Even leading-edge general machine learning handbooks such as [26–28] include extensive presentations of MCS concepts and architectures.. Rephrasing it. • Combined classifier can outperform the best individual classifier . The popularity of this approach is confirmed by the growing trend in the number of publications shown in Fig.g. Advantages. Intuitive reasoning justifies that the worst classifier would be out of the selection by this method  e.g. Such strategy significantly decreases the exploitation cost of whole classifier system. Specifically. 14] which are able to produce strong classifiers . MCS allow to train classifiers on dataset’s partitions and merge their decision using appropriate combination rule .region and by the linear one for the remaining objects. • Many machine learning algorithms are de f acto heuristic search algorithms. are highlighted by review articles as a hot topic and promising trend in pattern recognition [17–21]. and some of the keywords give as many references as in the previous five years. might be incorrect due to a small training set. (b) combining classifiers trained starting from different initial conditions could overcome the local optima problem. In the scarcity case. 2. assigning to each partition region an individual classifier that achieves the best classification accuracy over it. and (c) the true function may be impossible to be modeled by any single hypothesis.
5 decision . On the other hand. The most informative or discriminant features describing the objects are input to the classifier ensemble. An appropriate fusion method combines the individual classifier outputs optimally to provide the system decision. Another attractive area of implementation solutions is distributed computing systems (i. thus it is not possible to design a single classifier which outperforms another ones for each classification tasks.The main issues in MCS design are: • System topology: How to interconnect individual classifiers. the so-called coverage optimization approach tries to cover the space of possible models by the generation of a set of mutually complementary classifiers whose combination provides optimal accuracy. especially when a database is partitioned for privacy reasons  so that partial solutions must be computed on each partition and only the final decision is available as the combination of the networked decision. ensemble classifer 1 object classifier 2 fuser classifier n Figure 3: Overview of multiple classifier system. On one hand. • MCS can easily be implemented in efficient computing environments such as parallel and multithreaded computer architectures .: P2P. the so-called decision optimization approach concentrates on designing and training an appropriate decision combination function over a set of individual classifier given in advance . MCS try to select always the local optimal model from the available pool of trained classifiers. 36]. two main MCS design approaches can be distinguished. System structure. Grid or Cloud computing) [35. • Ensemble design: How to drive the generation and selection of a pool of valuable classifiers.e. • Fuser design: How to build a decision combination function (fuser) which can exploit the strengths of the selected classifiers and combine them optimally. According to Ho . formed by a set of complementary and diverse classifiers. is equivalent to a multistart local random search which increases the probability of finding an optimal model. The general structure of MCS is depicted in Fig.could start its work from different initial points of the search space. 3 following a classical pattern recognition  application structure. • Wolpert stated that each classifier has its specific competence domain  where they overcome other competing algorithms.
each time presenting it with an updated distribution over the training examples. when we consider each rule as the classifier. individual classifiers are applied in sequence. and so on. AdaBoost finds a new distribution that is closest to the old one but taking into consideration the restriction that the new distribution must be orthogonal to the mistake vector of the current weak learner. Alternatively. serial (bottom). The algorithms performs training of the weak learner multiple times. each classifier is feed the same input data. widely applied in data mining problems . in the serial (or conditional) topology. This model can be applied to classifiers with the so-called reject option as well . and secondary classifiers have higher exploitation cost . In the words of Kivinen et. so that the primary classifier is the computationally cheapest one. classifer 1 classifier 2 object fuzer decision classifier n object object classifer 1 classifer 1 classifer 2 classifer 2 classifer n classifer n decision decision Figure 4: The canonical topologies of MCSs: parallel (top). 42]. i. even weak learning algorithms with an accuracy slightly better than chance. al . thus the name AdaptiveBoost.. so that uncertain data samples are sent to a second classifier. so that the final decision of the combined classifier output is made on the basis of the outputs of the individual classifiers obtained independently. specialized in difficult instances. because of the low support/confidence in its result. In this architecture. The overwhelming majority of MCS reported in the literature is structured in a parallel topology . 4 illustrates the two canonical topologies employed in MCS design. adding classifiers in sequence. The novelty of Adaboost lies in the adaptability of the successive distributions to the results of the previous weak learners. trying to achieve a hard margin distribution. The distribution is altered so that hard parts of the feature space have higher probability.2.e. This topology is adequate when the cost of classifier exploitation is important. Shapire  showed that weak learners can be boosted into a strong learning algorithm by sequentially focusing on the subset of the training data that is hardest to classify. A very special case of sequential topology is the Adaboost introduced by Freund and Schapire in 1995 . In  the first classifier in the pipeline gives an estimation of the certainty of the classification. When the primary classifier can not be trusted to classify a given object e. The decisions generated by the weak learners are combined into a final single decision. System topology Fig. implying some kind of ranking or ordering over them. The goal of boosting is to enhance the accuracy of any given learning algorithm.g. then the data is feed to a secondary classifier [41. We notice the similarity of such approach to the ordered set of rules  or decision list . 6 .
Sharkley et al. and coincident failure diversity .  notice that we can ensure diversity using implicit or explicit approaches. disagreement  and double-fault measure [61.  argue that this hierarchy is not appropriate when the ensemble diversity varies between feature subspaces. 65]. as confirmed by the wide range of experimental results presented e. such as Kohavi-Wolpert variance . while explicit approaches focus on the optimization of a diversity metric over a given ensemble line-up. In  authors decomposed the error of the classification by majority voting into individual accuracy.. coincident failures. Brown et al.1. kappa-statistics .g. Unfortunately. it is intuitive that increasing diversity should lead to the better accuracy of the combined system. expecting an increase in diversity and a decrease in classifier output correlation . whereas the bad diversity has the opposite effect. Implicit approaches include techniques of independent generation of individual classifiers. • Non-pairwise diversity measures comparing outputs of a given classifier and the entire ensemble. On the one hand. For classification problems such theoretical results have not been proved yet.  proposed a hierarchy of four levels of diversity according to the answer of the majority rule. the problem of how to measure classifier diversity is still an open research topic. Ensemble design Viewing MCS as a case of robust software [52–55]. Brown et al. a measure of inter-rater (inter-classifier) reliability .  showed a functional relation between diversity and individual regressor accuracy. individual classifier training is performed conditional to the previous classifiers with the aim of exploiting the strengths of valuable members of classifier pool. Classifier ensemble design aims to include mutually complementary individual classifiers which are characterized by high diversity and accuracy . They relate the classifier selection to a NP-complete matrix cover problem.3. and possibility of at least one correct answer of ensemble members. and the procedures followed to ensure diversity in the ensemble. generalized diversity . Brown et al. because it was proved that the error of a compound model based on a weighted averaging of individual model outputs can be reduced according to increasing diversity [56. The analysis of several diversity measures  relating them to the concept of classifiers’ margin. The emphasis from the Hybrid Intelligent System point of view is in building MCS from components following different kinds of modeling and learning approaches. in . the measure of difficulty . allowing to control the bias-variance tradeoff systematically. such as Q-statistic . In this second kind of approaches. The good diversity has positive impact on ensemble error reduction. but there is no formal proof of this dependency . Diversity measures For regression problems. 59]. This section discusses some diversity measures. the variance of the outputs of ensemble members is a convenient diversity measure. the entropy measure . diversity arises as the guiding measure of the design process. however many diversity measures have been proposed till now. showed their limitations and the source of confusing empirical results. often based on random techniques. 3. They formulated the following taxonomy of diversity measures: • Pairwise measures averaging a measure between each classifier pair in an ensemble. implying that ensemble design in fact a 7 . good and bad diversities.
and competitive crossvalidation techniques . reinforcement learning . several hybrid approaches have been proposed such as heuristic techniques [72. These methods try to ascertain if a set of weak classifier may produce a strong one. Data features. To deal with the high computational complexity of ensemble pruning. based on feature space splitting and then assigning two-class classifiers (i. 64. Using different sets of features. Bagging applies sampling with replacement to obtain independent training datasets for each individual classifier. such as data privacy. Some authors take it into consideration during the component classifier selection step [78. or the need to learn over distributed data chunks stored in different databases [80–82]. It is worth pointing out the interesting propositions dedicated one-class classifier presented by Nanni  or an hierarchical method of ensemble forming. 91].2. The classifier ensemble are train on the basis of the obtained set. outputs. 8 . imposing to resort to sampling techniques to obtain manageable dataset partitions. have their origin in bootstrapping . 75]. The Random Subspace [86. making the final decision by a weighted voting rule. They may be compelled by several reasons. To ensure privacy we can train individual classifiers on each database independently and merge their outputs using hybrid classifier principles . For classification tasks.2. May be selected to ensure diversity training of a pool of classifiers. 79]. A huge database may impede to deliver trained classifiers under specified time constraints. Using different data partitions. and then creates random projections of a given training set by random selection of feature subsets. we should notice that using distributed data may come up against legal or commercial constraints which do not allow sharing raw datasets and merging them into a common repository . Diversity measures usually employ the most valuable subensemble in ensemble pruning processes . 87] was employed for several types of the individual classifiers such as decision tree (Random Forest) . Most popular techniques. Data partitions. 19.e. Diversifying input data This diversification strategy assumes that classifiers trained on different (disjoint) input subspaces become complementary. 2. The distributed data paradigm is strongly connected with the big data analysis problem . Taking into consideration the local specialization of individual classifiers. Three general strategies are identified: 1. Ensuring Diversity According to [22. Providing individualized train datasets for each classifier is convenient in the case of shortage of learning examples. 73]. or minimal distance classifier [90. 3. A well known approach is cross-validated committee which requires to minimize overlapping of dataset partitions . Regarding data privacy. 94]. focusing on difficult samples. linear classifiers . the cost of acquiring feature values (which could be interpreted as the price for examination or time required to collect the data for decision making) can be critical. Support Vector Machines) locally presented in [93. 3. 38] we can enforce the diversity of a classifier pool by the manipulation of either individual classifier inputs.1. evolutionary algorithms [74. such as bagging  or boosting [14. 3. Boosting modifies the input data distribution perceived by each classifier from the results of classifiers trained before. 85]. Attribute Bagging  is a wrapper method that establishes the appropriate size of a feature subset. or models.quite difficult combinatorial problem.
In more general approaches. so that an individual classifier is designed to classify only some classes in the problem. Adaptive Splitting and Selection algorithm in  partitions the feature space and assigns classifiers to each partition into one integrated process.g. In [109. Recently several interesting propositions on how to combine the binary classifiers were proposed. the combination of individual outputs is made by finding the closest class. Lee et al. Additionally. Both static [111–113] and dynamic [114–116] classifier specialization are widely used for data stream classification.Local specialization. selecting (or training) a classifier for each partition. The main advantage of AdaSS is that the training algorithm considers an area contour to determine the classifier content and. The most popular propositions of two-class classifier combinations are: OAO (one-against-one) and OAA (one-against-all). It gives the MCS answer for all objects included in the partition . 72. ECOC (Error Correcting Output Codes) model was proposed by Dieterich and Bakiri . Static and dynamic approaches are distinguished: • Static classifier selection : the relation between region of competence and assigned classifier is fixed.2. by using preselected committee of the individual classifier and making the final decision on the basis of a voting rule . a multi-class classification problem can be decomposed into a set of binary classification problems [117. who assumed that a set of classifiers produces sequence of bits which is related to code-words during training. In the OAA method. 96–98].2. and selects the best individual classifier for each cluster according to its local accuracy. Some proposals assume classifier local specialization.. 9 .  used pairwise coupling. Friedman employed Max-Win rule . Diversifying outputs MCS diversity can be enforced by the manipulation of the individual classifier outputs.  used the fuzzy entropy measure to partition the feature space and select the relevant features with good separability for each of them. 3. It is assumed for classifier selection. providing only locally optimal solutions [38. OAA returns class with maximum support. • Dynamic classifier selection: the competencies of the individual classifiers are calculated during classification operation [104–107]. while others divide the feature space. Kuncheva’s Clustering and Selection algorithm partitions the feature space by a clustering algorithm. 118].g. a classifier is trained to separate a chosen class from the remaining ones. The model that a given object belongs to a chosen class is tested against the alternative of the feature vector belonging to any other class.. Passerini et al. conversely. 110] authors propose dynamic ensemble selection based on the original competence measure using classification of so-called random reference classifier. Wu et al. in some sense. to the code given by the outputs of the individual classifiers. There are several interesting proposals which extend this concept e. The ECOC points at the class with the smallest Hamming distance to its codeword. showed advantages of this method over traditional ones for the ensemble of support vector machines . that the region shapes adapt to the competencies of the classifiers. The combination method should restore the whole class label set e. the majority voting or more sophisticated rules are proposed as combination method of area classifiers . selecting the best single classifier from a pool of classifiers trained over each partition of the feature space. where at least one predictor relates to each class.
. However. To combine such classifiers the typical methods developed for binary ones are used  but it is worth mention the work by Wilk and Wozniak where authors restored multi-class classification task using a pool of one-class classifiers and the fuzzy inference system . so that the answer is given if majority is greater than half the pool of classifiers. We could use the different versions of the same model as well. A simple example shows the risks of the Oracle model: assume we have two classifiers for a binary class problem. It worth mentioning the one-class classification model which is the special case of binary classifier trained in the absence of counterexamples. Class label fusion Early algorithms performing fusion of classifier responses [9. We can combine the class labels but in the case of continuous outputs we have to normalize them e. Fuser design Some works consider the answers from a given Oracle as the reference combination model . built such that if at least one of the individual classifiers provides the correct answer. The Oracle is an abstract combination model. • majority voting. Some researches used the Oracle in comparative experiments to provide a performance upper bound for classifier committee  or information fusion methods . Let us systematize methods of classifier fusion. Diversifying models Ensembles with individual classifiers based on different classification models take advantage of the different biases of each classifier model . The combination methods dedicated the one-class classifiers still await a proper attention . Regarding neural networks  it is easy to train pools of networks where each of them is trained starting from randomly chosen initial weights. using fuzzy approach . As a consequence the Oracle model does not fit in the Bayesian paradigm.3. which on the one hand could use class labels or support function. hence the Oracle will always return the correct answer. 61] only implemented majority voting schemes in three main versions : • unanimous voting. 10 . Its main goal is to model normality in order to detect anomaly or outliers from the target class . Alternatively. Regarding decisions tree we can choose randomly the test for a given node among the possible tests according to the value of a splitting criterion. because many machine learning algorithms do not guarantee to find the optimal classifier. The taxonomy of decision fusion strategies is depicted in Fig. the combination rule should be carefully chosen. Raudys  noticed that Oracle is a kind of quality measure of a given individual classifier pool. 5. 10. taking the answer with the highest number of votes. a pool of classifiers can be produced by noise injection. A comprehensive recent survey of binary classifier ensembles is .g. so that the answer requires that all classifiers agree.1. 4. a random one and the other that always returns the opposite decision. 3.H¨ullermeier proposed the adaptive weighted voting procedure .2. • simple majority. Combining the results of various initializations may give good results. then the MCS committee outputs the correct class too. 4. on the other hand combination rules could be given or be the results of training.
and. 6. proven as the Condorcet Jury Theorem . . computed either as a neural network output. 133]. the classifier and the class. Alternative voting methods weight differently the decisions coming from different committee members [22. both to the classifier and the class. The typical architecture of combined classifier based on class labels is presented in the left diagram of Fig. Support function fusion Support function fusion system architecture is depicted in Fig. There are many works following this approach. The value of a support function is the estimated likelihood of a class. 6. 136] as depicted in the right diagram of Fig. such as 11 . . no one of these models can improve over the Oracle. To achieve that we need additional information. Later works focused on the analytically derived classification performance of combined classifiers hold only when strong conditions are met  so that they are not useful from practical point of view. . to features values.NONTRAINABLE TRAINABLE (separately or co-trained) BASED ON CLASSLABELS BASED ON SUPPORT FUNCTIONS Figure 5: A taxonomy of fusing strategies for the combination of MCS individual decisions. produced by the probabilistic models embodied by the classifiers [137–139]. The right diagram corresponds to a MCS using additional information from the feature values. Support functions provide a score for the decision taken by an individual classifier. classifer 1 classifer 1 classifier 2 object decisi on decision fuser (combination rule) . the Borda count  computes an score for each class on the basis of its ranking by each individual classifier. 7. 135.2. 4. classifier n classifier decision decision decision features Figure 6: Architecture of the MCS making decision on the basis of class label fusion only (left diagram). . such as the feature values [132. First to be mentioned. or fuzzy membership function. finally. a posteriori probability. In  authors distinguished the types of weighted voting according to the classifier. Anyway. The expected error of majority voting (for independent classifiers with the same quality) was estimated in 1794 according to Bernoulli’s equation. classifier n classifier 2 object decision decisi on decision . The most popular form of support function is the a posteriori probability .
153]. This method trains combination block using individual classifier outputs presented during classification of the whole training set. The aggregating methods use simple operators such as supremum or the mean value. Zheng used data envelopment analysis . (c) on the classifier and the class. 136]. 147] which combines classifier outputs using so-called input dependent gating function. it is possible to improve the Oracle  using any of these aggregation methods. and the feature vector.e. For many-class problems. 4. Trainable Fuser Fuser weight selection can be treated as a specific learning process [31. Concept drift Before entering the discussion of practical applications we consider a very specific topic of real life relevance which is known as Concept Drift in knowledge engineering domains. .3. According to . they have little practical applicability because of the hard conditions imposed by them . and Na¨ıve Bayes as the MCS combination method . Shlien  used Dempster and Shafer’s theory to reach a consensus on the weights to combine decision trees. Wozniak  trained the fuser using perceptron-like learning. object classifer 1 supports for each class classifier 2 supports for each class . the class. Tresp and Taniguchi  proposed a linear function for this fuser model. We have to mention the group of combination methods built from pools of heterogenous classifiers. Most of the combination methods do not take into consideration possible relations among individual classifiers. Huang and Suen  proposed Behavior-Knowledge Space method which aggregates the individual classifiers decision on the basis of the statistical approach. and Cheeseman  proposed a mixture of Gaussian. The main aggregating advantage is that it counteracts over-fitting of individual classifiers. 5. classifier n count common supports for each class and make decision according to them decision supports for each class Figure 7: Architecture of the MCS which computes the decision on the basis of support function combination. evolutionary algorithm [152. However. Most of the conventional 12 . another salient approach is the mixture of experts [146. the following types of weighted aggregation can be identified depending on: (a) only the classifier id. or non-stationary processes in signal processing and statistics domains. 143. For two-class recognition problems only the last two types of aggregation allow to produce compound classifier which may improve the Oracle. They do not involve learning. i. 144]. . Some analytical properties and experimental evaluations of aggregating methods were presented in [10. such as stacking . and (d) on the classifier. Other fuser trainable methods may be strictly related to ensemble pruning methods. 31. when authors use some heuristic search algorithm to select the classifier ensemble. as [72. Finally. 141] according to the chosen fuser. (b) the classifier and the feature vector.the optimal projective fuser of . using different classification models. the combination of neural networks outputs according to their accuracy .
buildings. i. Not all classification algorithms dealing with concept drift require drift detection. Depending on the application. water. 165]. A difficult problem is drift detection. We review in this section recent applications to remote sensing data. In the works revised below. When a classifier weight is too small. Machine learning methods in security applications (like spam filters or IDS/IPS)  or decision support systems for marketing departments  require to take into account new training data with potentially different statistical properties. The occurrence of Concept Drift decreases the true classification accuracy dramatically. computer security. so-called heterogeneous MCS.classifiers do not take into consideration this phenomenon. The most popular approaches are the Streaming Ensemble Algorithm (SEA)  and the Accuracy Weighted Ensemble (AWE). present the Dynamic Weighted Majority (DWM) algorithm  which modifies the decision combination weights and updates the ensemble according to number of incorrect decisions made by individual classifiers. recommender systems. financial risk assessment. vegetation. a new classifier is trained and added to the ensemble in its place. a few general classes may be identified. and medical computer aided diagnosis. are composed of classifiers of the same kind. such as Random Forest (RF). The best performing classifiers are selected to constitute the MCS committee in the next time epoch. A recent review appears in . geology. roads. i. because the object category or its properties will be changing. Concept Drift means that the statistical dependencies between object features and its classification may change in time. 6. so that future data may be badly processed if we maintain the same classification. Incoming data are collected in data chunks. Probabilistic Neural Networks (PNN). then it is removed from the ensemble. Land cover mapping consists in the identification of materials that are in the surface of the area being covered. MCS are specially well suited to deal with Concept Drift. The current research direction is to propose an additional binary classifier giving the decision to rebuild the classifiers. basic classifiers are Multi-Layer Perceptron (MLP). infrastructure degradation assessment. Concept drift occurs frequently in real life . identifying tree or crop types. Radial Basis Function (RBF). which are used to train new models. or a more precise classification can be required. Change detection consists in the identification 13 . whereas the AWE uses a weighted voting strategy. Applications Reported applications of classifier ensembles have grown astoundingly in the recent years due to the increase in computational power allowing training of large collections of classifiers in practical application time constraints. k-Nearest Neighbor (kNN). Support Vector Machines (SVM). Sometimes the works combine diverse kinds of classifiers. forestry. Remote sensing The main problems addressed by MCS in remote sensing domains are the land cover mapping and change detection.e. Homogeneous MCS.e. fraud detection. As the decision rule. urban planning. Applications include agriculture. The drift detector can be based on changes in the probability distribution of the instances [161–163] or classification accuracy [164.1. the SEA uses a majority voting. and Maximum Likelihood (ML) classifiers. Kotler et al. 6. The individual classifiers evaluation is done on their accuracy on the new data. which is the problem of deciding that the Concept Drift has taken place. because they can adjust the model to incoming data [? ].
Works on hyperspectral images acquired by the HyMap sensor have been addressed to build vegetation thematic maps . as well as two feature selection methods: the out-of-bag and a best-first search wrapper feature subset selection method. it implies the computation over time series of images. testing was performed on multi-source data. and other ancillary information. slope and aspect data are used as input features. etc. The MCS was heterogenous. SAR can provide also multivariate data from varying radar frequencies. Landsat Multi-Spectral. using sequential Gaussian simulation and the clustering of species into vegetation types. but land is easily occluded by weather conditions.of places where the land cover has changed in time. Interestingly. Further work with RF has been done assessing the uncertainty in modeling the distribution of vegetation types . Samples with higher membership coefficient are added to the corresponding class data. i. the approach performs clustering of classifier error. to compute the land cover map. comparing RF and decision tree-based Adaboost. Artificial data is generated from the Gaussian modeling of the data distribution. i. The application of RF to SAR multitemporal data aims to achieve season invariant detection of several classes of land cover. 65.e. and the general conclusion is that tree ecotopes are better discriminated than grass ecotopes. composed of MLP. ceral. The RF approach is able to successfully fuse these inhomogeneous informations. so that observations are better suited for continuous monitoring of seasonally changing land covers. Dealing with labeled data scarcity. grassland. which is applied to Landsat multispectral data. there are methods  based on the combination of RF and the enrichment of the training dataset with artificially generated samples in order to increase classifier diversity. such as the measurements of environmental sensors. Optical data has better interpretability by humans. The approach was proven to be optimal under some conditions on the classifiers. Remote sensing classification can be done on a variety of data sources. It has been applied to estimate land cover on Landsat data over Granada. The application of RF to processing remote sensing data has been abundant in the literature. performing classification on the basis of environmental variables. The success of RF for remote sensing images has prompted the proposal of an specific computational environment . Ensambles of SVM have been also applied to land cover map. cloud formations. sometimes performing fusion of different data modalities. composing the pixel’s feature vector of joining multispectral with radar data channels. where the model trained on one SAR image was applied on other SAR images of the same site obtained at different times. Change detection may or may not be based on previous or separate land cover maps. Images were coregistered and some model portability was tested. Hyperspectral sensing provides high-dimensional data at each image pixel. Spain  and multi-source data in a Colorado mountainous area . Specifically. aggregating similar classifiers. and PNN. 167]. the ground truth data scarcity has been attacked by an active learning approach to semi-supervised SVM training . Indeed. in an approach that combines spatial distribution modeling by spatial interpolation. To avoid the combinatorial complexity.e. forest.. These semi-supervised SVM are combined in a majority voting ensemble 14 . elevation. Land cover mapping Early application of MCS to land cover mapping consisted in overproducing a large set of classifiers and searching for the optimal subset [38. Other data sources are elevation maps. with high spectral resolution. Synthetic Aperture Radar (SAR) is not affected by weather or other atmospheric conditions. RF performed best. RBF. and the classifier is retrained in an iterative process. Diverse feature subsets are tested. with lowest spatial variability. The active learning approach is based on the clustering of the unlabeled data samples according to the clustering of the SVM outputs on the current training dataset.
The sequential process of the images previous to classification includes pan-sharpening of the multi-temporal images. refraction and reflection effects introduced by the water cover. e-healthcare and e-governance can be very sensitive to this type of attacks.2. The MCS constituent classifiers may be ANNs trained with robust learning algorithms. A critical issue of the approach is the need to report validation results. where classifier fusion was performed either by majority voting. A general architecture for automatic detection of DDoS attacks is needed where the attack detection may be performed by a MCS. i. specifically MLP. Polarimetric SAR data used for the classification of Boreal forests require an ensemble of SVM . Change detection Early application of MCS to land cover change detection was based on non-parametric algorithms. and the machine learning approaches are trying to provide predictive solutions that may allow to avoid the negative impact of such attacks. and the Fuzzy Integral  . Land cover classification in the specific context of shallow waters has the additional difficulties of the scattering. service unavailability. There are some public available 15 . and revenue loss. co-registration. such as military applications. where initial classification results obtained by SVM are refined in a second SVM classifier and the final result is given by a linear combination of two ensembles of SVM classifiers and a minimum distance classifier. 6. a boosting strategy is defined on the ensemble of RBP trained ANNs. from universities. Besides. The approach is applied to Landsat images for the estimation of coral population in coastal waters.and applied to the classification SPOT and Landsat optical data. and a Neyman Pearson approach is used to make the final decision . There is a need for intelligent systems able to discriminate legitimate flash crowds from an attack. Recent works on change detection in panchromatic images with MCS follow three different decision fuser strategies: majority voting. Secure information processing is a growing concern. RBF. joining two images from two different seasons (summer and winter) and performing the feature selection and training on the joint data vectors. Computer Security Computer security is at the core of most critical services nowadays. which can only be based on recorded real life DDoS attacks. which can produce network performance degradation. This architecture may be based on Sugeno Adaptive Neuro-Fuzzy Inference Systems (ANFIS)  . Testing data were Thematic Mapper multispectral images. Dempster-Shafer evidence theory. banking. Specifically. A robust process combines a parallel and a serial architecture . Here we introduce some of the problems. with specific feature selection process. Resilient BackPropagation (RBP). and a final MCS decision computed on the multi-spectral data and the change detection data obtained from the various pan-sharpening approaches. Each of the SVM is specifically tuned to a class. with current solutions proposed from the MCS paradigm. Distributed service providers. Best results are obtained when multi-temporal data is used.e. and the Synthetic Aperture Radar (SAR) of Landsat 5 satellite. 179] . k-NN. companies. Distributed denial of service Distributed denial of service (DDoS) are among the most threatening attacks that an Internet Service Provider may face. Bayesian average and maximum a posteriori probability. communication. raw radiometric change detection by image subtraction and automatic thresholding. the system estimates the water depth by a bathymetry estimation process. and ML classifiers [178.
Classifiers learn patterns in the known malicious codes extrapolating to yet unseen codes. The problem is attacked by modular MCS whose compounding base classifiers are one-class classifiers built by the Parzen window probability density estimation approach . Anomaly detection in WSN is performed using an ensemble of binary classifiers. Finally. such as trojans. low power devices deployed over a geographical space for monitoring. Feature selection processes. where decision trees and support vector machines are combined as a hierarchical hybrid intelligent system model. each tuned on diverse parameters and built following a different approach (Average. The interesting proposed hybrid intrusion detection was presented in . and others like portable executable features. Predictive approaches based on Machine Learning techniques may allow anticipative detection at the cost of some false positives. A taxonomy of such approaches is given in . results reported on these datasets may not be informative of the system performance on new attacks which may have quite different features. strings. Results have been reported that MCS overcome other approaches. However. ANFIS). Clustering of n-grams is performed to obtain a model of the normal communication behavior which is accurate allowing zero-day detection of worm infection even in the case of low payload or slow penetration . neural network. 16 . are better suitable for active learning needed to keep the classifiers updated and tuned to the changing malicious code versions. On the other hand. therefore some degree of damage has already been done. autorregresive. Malware Malicious code. Wireless sensor networks Wireless sensor networks (WSN) are collections of inexpensive. Intruder detection performed as an anomaly detection process allows to detect previously unseen patterns. measuring and event detection. The decision is made by a weighted combination of the classifiers outputs . An approach relies on the fact that Instruction Set Randomization prevents code injection attacks. describing the basic code representation by byte and opcode n-grams. Anomalies in the WSN can be due to failures in software or hardware. detection by anti-virus approaches can only be performed after some instance of the code has been analyzed finding some kind of signature. or to malicious attacks compelling the sensors to bias or drop their information and measurements. contrary to signature based approaches. so that detected injected code can be used for adaptation of the anomaly classifier and the signature-based filtering . Intrusion Prevention tries to impede the execution of the intruder code by fail-safe semantics.datasets to perform and report these results. This is a pervasive concern in all security applications of machine learning algorithms. Intrusion detection Intrusion Detection and Intrusion Prevention deal with the identification of intruder code in a networked environment via the monitoring of communication patterns. so that different thresholds can be tuned for each module allowing some optimization of the false alarm rate. automatic response and adaptive enforcement. at the cost of false alarms. virus. Each module is specialized in a specific protocol or network service. classifiers tested in this problem include a wide variety of MCS combining diverse base classifiers with all standard fuser designs. are applied to find the most informative features. such as the Fisher score. spyware.
the intelligent processing of financial information. scarcity and incompleteness of data. including sensitivity to noise added to the attributes . Fraud detection Fraud detection involves identifying fraud as soon as possible after it has been perpetrated. The exploration of the sensitivity to the ratio of fraud to non-fraud of the random undersampling approach to deal with unbalanced class sizes is required to validate the approaches. ETB consists in the iterative selection of subsets of samples based on their error under the current classifier. In this section we review some of the most important issues. money laundering. An special case of credit risk is enterprise risk assessment which has a strong economic effect due to the financial magnitude of the entities involved. credit risk. which has provided techniques to counteract fraudsters in credit card fraud. and outlier detection techniques are required to detect the manipulation events. Other approaches to this problem include a bagged ensemble of SVM tested on a british card application approval dataset . gathering current attempts to the deal with them.6. the assessing of financial or credit risks. and computer intrusion. Credit card fraud Specific works on credit card fraud detection use real-life data of transactions from an international creditcard operation . To deal with 17 . Credit risk Credit risk prediction models seek to predict whether an individual will default on a loan or not. Fraud detection  is big area of research and applications of machine learning. An innovative research track is the use of peer-group analysis for trade stock manipulation detection. Stock market Trade based stock market manipulation try to influence the stock values simply by buying and then selling. fraud detection In the current economical situation. Comparing RF against SVM and logisti regression . its peers . based on the detection of outliers whose dynamic behavior separates from that of the previously similar stock values. It is greatly affected by the unavailability. and bagging of probabilistic networks has been proposed as a general tool for fraud detection because the MCS approach improves the robustness of the normal behavior modeling . Dynamic clustering allows to track in time the evolution of the community of peers related to the stocks under observation. RF was the best performer in all experimental conditions as measured by almost all performance measurements. The application of machine learning to this problem includes the evaluation of bagging. It is difficult to detect because rules for detection quickly become outdated. stacking as well as other conventional classifiers over three benchmarking datasets. boosting. telecommunications fraud. Probabilistic networks are specific one-class classifiers that are well suited to this task. Banking. Developing new tools may allow to avoid in the future the dire problems faced today by society. MCS have been also applied successfully in this domain. and related issues have become a prime concern for society and for the computational intelligence community.3. Another approach for this problem is the Error Trimmed Boosting (ETB)  which has been tested over a privative dataset provided by a company. A key task is modeling the normal behavior in order to be able to establish suspicion scores for outliers.
New fraud trends Prescription fraud has been identified as a cause of substantial monetary loss in health care systems. To be adaptive to fraudster evolving strategies. Medicine Medicine is a big area of application of any innovative computational approach.4.e. business. Financial risks Uncertainty in the financial operations is identified with the financial risks such as credit. investment. giving a specific performance measures allow the exploration of computational solutions to these problems . The effect of PCA initial dimensionality reduction is also tested . The authors use a novel distance based on data-mining approach in a system which is capable of self-learning by regular updates. Bank performance and bankruptcy prediction is addressed using a widely heterogenous MCS including PNN. The analysis of social networks by means of MCS may allow the detection of fraud in automobile insurance. intended for money laundering. Several conventional classifiers and MCS have been tested in this framework using a large pool of datasets. The resulting method has increased diversity improving results over a dataset provided by the Bank of China . so here we only give a scrap of all 18 . and with very imprecise or ambiguous data in other situations. The system is designed to perform on-line risky prescription detection followed by off-line expert evaluation. clustering and classification pipeline. and a fuzzy rule system. and operational risks. Financial distress can be detected by clustering and MCS in four different combination models. i. The prediction of failure of dotcom companies has been a matter of research since the bubble explosion after the year two thousand. and boosted trees are the best performing approach. The research works need to real life data from a large multi-center medical prescription database . Clustering has been found to improve classification performance.RBF. Tuning a hybrid of PNN. it is required to emphasize online learning. A new brand of frauds appear in the online gaming and lotteries. Fraud in telecommunication systems involving usage beyond contract specifications is dealt with in  by a preprocessing. Experimental framework for the evaluation of financial risk assessment models. consisting in staging traffic accidents and issuing fake insurance claims to their general or vehicle insurance company . The effect of feature construction from previous experience and a priori information in the efficiency of classifiers for early warning of bank failures is reported in . Ensemble systems with diversity ensured by genetic algorithm based selection of component classifiers is proposed in  for bankruptcy prediction in South Korean firms. MLP and genetic programming classifiers over a set of features selected applying a t-test and F-test for relevance to the categorical variable has given some solutions . SVM. dealing with massive amounts of data in some instances. whose detection is dealt with a mixture of supervised and unsupervised classifiers . and online cluster detection. Clustering is performed by classical SOM and kmeans algorithms and used to partition the data space prior to MCS training  .this problem a combination of bagging and random subspace feature selection using SVM as the base classifier has been developed and tested. it consists in the prescription of unnecessary medicaments. 6. Bankruptcy prediction is a dramatic special case of credit risk. The range of applications is quite big. The same approach is reported in  to detect fraud in the financial statement of big companies. MLP. CART trees.
composed of SVM. there have approaches to provide CAD for this condition. the selection of features. bayesian networks and ANN  finding ten new biomarkers. It is used for the classification of image data searching for predictive non-invasive biomarkers that may allow early or prodromal diagnosis of a number of degenerative diseases which have increasing impact in the society due to the aging of populations around the world. the preprocessing of the data to normalize it and remove noise. such as the ANN. Classifier combination is done by majority voting or hierarchical fusion.the current problems and approaches related with the MCS paradigm. the k-neighborhood ALH and the SVM. The dysfunction or abnormality of one or more of the heart four valves is called valvular heart disease. which involve as the final step some kind of classifier predicting the subject’s disease or normal status. Coronary diseases A recent instance of CDSS is the application to cardiovascular disease diagnosis of an heterogenous collection of classifiers. variations of Adaboost . Early approaches consisted in hybrid systems. an ensemble of K-local hyperplanes based on random subspace and feature selection has been tested . Coronary artery disease is a broad term that encompasses any condition that affects the heart. Two separate ANNs are trained to identify myocardial infarction on training sets with different statistics regarding the percentage of patients in . 210] over features selected by a correlation analysis with the categorical variable. specifically for the classification of Alzheimer disease patients. Machine learning techniques have been proposed for three-dimensional protein structure prediction. In Medicine. It is a chronic disease in which the coronary arteries gradually harden and narrow. hybridizations of kernel and Dendritic Computing approaches . In this AptaCDSS-E process starts with the use of an aptamer biochip scanning protein expression levels which is the input to physician taking the decisions afterwards. the machine learning approach is gaining widespread acceptation. Doctor decisions are stored for system retraining. such as an RVM based two stage pipeline . Feature selection is performed by an ANOVA analysis. Neuroscience In the field of Neurosciences. and the final selection of the classifier. Many CAD systems related with coronary diseases are based on the information provided by the electrocardiogram (ECG). Its diagnosis is performed by neural network ensembles in [209. statistical classifier and case base reasoning classifier combined by majority voting of . Diverse MCS approaches have been applied to structural MRI data. a specific research area since the inception of Artificial Intelligence is the construction of Computer Aided Diagnosis (CAD) systems or Clinical Decision Support Systems (CDSS) . if the output is below a threshold the subject is deemed healthy. where feature selection is done according to distance to the class centroids. so that many of them rely on the features extracted from them. The network specialized in healthy controls is applied to the new data. such as the use of a mixture of three ANNs for the prediction of coronary artery disease . A recent approach is the MarFold  combining by majority voting three margin-based classifiers for protein fold recognition: the adaptive local hyperplane (ALH). otherwise the disease-specific network is applied to decide. Proteomics Proteins are said to have a common fold if they have the same major secondary structure in the same arrangement and with the same topology. Classifier 19 . there are several steps such as the definition of the sensor providing the data. In CDSS development. For instance.
There are several interesting works which apply the hybrid and combined approach to recommender systems.. The diversity is believed to provide improved accuracy and classifier performance. nonetheless we point out that classifier combination is not the only way to to produce hybrid classifier systems. However. Goksedef and Gundoz-Oguducu  combined the results of several recommender techniques based on Web usage mining. also known in the literature as combined classifier or classifier ensemble. so fruitful that our review could not be exhaustive. or to advice conference organizers about assigning papers to peer reviewers .5. resilience and robustness to high data dimensionality and diverse forms of noise. while Pazzani proposed to use a voting scheme . and many combinations of these ideas. 20 . 218] and its visual decoding . Billsus and Pazzani  selected the best recommendation on the basis of a recommendation quality metric as the level of confidence while Tran and Cohen  preferred an individual which is the most consistent with the previous ratings of the user. Jahrer and T¨oscher  demonstrated the advantage of ensemble learning applied to the combination of different collaborative filtering algorithms on the Netix Prize dataset. the fact is that MCSs show in most instances improved performance. such as labeling noise. Porcel et al. Burke  proposed hybrid recommender systems combining two or more recommendation techniques to improve performance avoiding the drawbacks of an individual recommender.  proposed Combined Collaborative Recommender based on three different collaborative recommender techniques. Such hybrid systems are the focus of intense research recently. which is the reconstruction of the visual stimuli from the fMRI data. 6.  performed a linear combination of the ratings obtained from individual recommender systems into one final recommendation. either theoretically or empirically. Claypool et al. Key issues related to the problem under consideration are classifier diversity and methods of classifier combination. boosting the data weights. Final remarks We have summarized the main research streams on multiple classifier systems. what was presented in this work. the diversity hypothesis has not been fully proven. Most works try to obtain maximum diversity by different means: introducing classifier heterogeneity. but such systems are expanding beyond typical sales. 7. Similar observations were confirmed by Balabanovic et al.  and Pazzani  who demonstrated that hybrid method recommentations improve collaborative and content-based approaches.  developed an hybrid fuzzy recommender system to help disseminate information about research resources in the field of interest of a user. learning sets and human expert rules to improve rules on the basis of incoming data). They are used to predict which mobile telephone subscribers are in risk of switching to another provider. bootstrapping the training data. Recommender systems Nowadays. The there are several propositions how to combine the classifier outputs.Ensembles have been applied to the classification of fMRI data [217. We envisage further possibilities of hybridization such as • Merging the raw data from different sources into one repository and then train the classifier. • Merging the raw data and a prior expert knowledge (e. Kunaver et al. randomizing subspace projections. randomizing feature selection.g. recommender systems are the focus of intense research . Nowadays. They try to help consumers to select the product that may be interesting for them based on their previous searches and transactions.
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40000 combined classifier 35000 multiple classifier system 30000 classifier ensemble 25000 classifier fusion hybrid classifier 20000 15000 10000 5000 0 till 1990 4000 1991-1995 1996-2000 2001-2005 2006-2010 2011-2012 combined classifier 3500 multiple classifier system 3000 classifier ensemble 2500 classifier fusion hybrid classifier 2000 1500 1000 500 0 till 1990 2500 1991-1995 1996-2000 2001-2005 2006-2010 2011-2012 combined classifier multiple classifier system 2000 classifier ensemble classifier fusion 1500 hybrid classifier 1000 500 0 till 1990 1991-1995 1996-2000 2001-2005 2006-2010 2011-2012 .

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