waashk/dblp huggingface dataset:
a robust discriminative term weighting based linear discriminant method for text classification. text classification is widely used in applications ranging from e-mail filtering to review classification. many of these applications demand that the classification method be efficient and robust, yet produce accurate categorizations by using the terms in the documents only. we present a supervised text classification method based on discriminative term weighting, discrimination information pooling, and linear discrimination. terms in the documents are assigned weights according to the discrimination information they provide for one category over the others. these weights also serve to partition the terms into two sets. a linear opinion pool is adopted for combining the discrimination information provided by each set of terms yielding a two-dimensional feature space. subsequently, a linear discriminant function is learned to categorize the documents in the feature space. we provide intuitive and empirical evidence of the robustness of our method with three term weighting strategies. experimental results are presented for data sets from three different application areas. the results show that our method's accuracy is higher than other popular methods, especially when there is a distribution shift from training to testing sets. moreover, our method is simple yet robust to different application domains and small training set sizes.
mining periodic behavior in dynamic social networks. social interactions that occur regularly typically correspond to significant yet often infrequent and hard to detect interaction patterns. to identify such regular behavior, we propose a new mining problem of finding periodic or near periodic subgraphs in dynamic social networks. we analyze the computational complexity of theproblem, showing that, unlike any of the related subgraph mining problems, it is polynomial. we propose a practical, efficient and scalable algorithm to find such subgraphs that takes imperfect periodicity into account. we demonstrate the applicability of our approach on severalreal-world networks and extract meaningful and interesting periodic interaction patterns.
variance minimization least squares support vector machines for time series analysis. here we propose a novel machine learning method for time series forecasting which is based on the widely-used least squares support vector machine (ls-svm) approach. the objective function of our method contains a weighted variance minimization part as well. this modification makes the method more efficient in time series forecasting, as this paper will show. the proposed method is a generalization of the well-known ls-svm algorithm. it has similar advantages like the applicability of the kernel-trick, it has a linear and unique solution, and a short computational time, but can perform better in certain scenarios. the main purpose of this paper is to introduce the novel variance minimization least squares support vector machine (vmls-svm) method and to show its superiority through experimental results using standard benchmark time series prediction datasets.
graph-based rare category detection. rare category detection is the task of identifying examples from rare classes in an unlabeled data set. it is an open challenge in machine learning and plays key roles in real applications such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. in this paper, we develop a new graph-based method for rare category detection named grade. it makes use of the global similarity matrix motivated by the manifold ranking algorithm, which results in more compact clusters for the minority classes; by selecting examples from the regions where probability density changes the most, it relaxes the assumption that the majority classes and the minority classes are separable. furthermore, when detailed information about the data set is not available, we develop a modified version of grade named grade-li, which only needs an upper bound on the proportion of each minority class as input. besides working with data with structured features, both grade and grade-li can also work with graph data, which can not be handled by existing rare category detection methods. experimental results on both synthetic and real data sets demonstrate the effectiveness of the grade and grade-li algorithms.
nearest neighbour classifiers for streaming data with delayed labelling. we study streaming data where the true labels come with a delay. the question is whether the online nearest neighbour classifier (ib2 and ib3 here) should employ the unlabelled data. three strategies are examined: do-nothing, replace and forget. experiments with 28 data sets show that ib2 benefits from unlabelled data, while ib3 does not.
maximum margin embedding. we propose a new dimensionality reduction method called maximum margin embedding (mme), which targets to projecting data samples into the most discriminative subspace, where clusters are most well-separated. specifically, mme projects input patterns onto the normal of the maximum margin separating hyperplanes. as a result, mme only depends on the geometry of the optimal decision boundary and not on the distribution of those data points lying further away from this boundary. technically, mme is formulated as an integer programming problem and we propose a cutting plane algorithm to solve it. moreover, we prove theoretically that the computational time of mme scales linearly with the dataset size. experimental results on both toy and real world datasets demonstrate the effectiveness of mme.
a hierarchical algorithm for clustering uncertain data via an information-theoretic approach. in recent years there has been a growing interest in clustering uncertain data. in contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. in this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering schemes have drawn less attention.we propose a centroid-linkage-based agglomerative hierarchical algorithm for clustering uncertain objects, named u-ahc. the cluster merging criterion is based on an information-theoretic measure to compute the distance between cluster prototypes. these prototypes are represented as mixture densities that summarize the pdfs of all the uncertain objects in the clusters. experiments have shown that our method outperforms state-of-the-art clustering algorithms from an accuracy viewpoint while achieving reasonably good efficiency.
sequence mining automata: a new technique for mining frequent sequences under regular expressions. in this paper we study the problem of mining frequent sequences satisfying a given regular expression. previous approaches to solve this problem were focusing on its search space, pushing (in some way) the given regular expression to prune unpromising candidate patterns. on the contrary, we focus completely on the given input data and regular expression. we introduce sequence mining automata ($sma$), a specialized kind of petri net that while reading input sequences, it produces for each sequence all and only the patterns contained in the sequence and that satisfy the given regular expression. based on this automaton, we develop a family of algorithms. our thorough experimentation on different datasets and application domains confirms that in many cases our methods outperform the current state of the art of frequent sequence mining algorithms using regular expressions (in some cases of orders of magnitude).
estimating aggregates over multiple sets. many datasets, including market basket data, text or hypertext documents, and measurement data collected in different nodes or time periods, are modeled as a collection of sets over a ground set of (weighted) items. we consider the problem of estimating basic aggregates such as the weight or selectivity of a subpopulation of the items. we extend classic summarization techniques based on sampling to this scenario when we have multiple sets and selection predicates based on membership in particular sets.
enhancing the stability of spectral ordering with sparsification and partial supervision: application to paleontological data. recent studies have demonstrated the prospects of data mining algorithms for addressing the task of seriation in paleontological data (i.e. the age-based ordering of the sites of excavation). a prominent approach is spectral ordering that computes a similarity measure between the sites and orders them such that similar sites become adjacent and dissimilar sites are placed far apart. in the paleontological domain, the similarity measure is based on the mammal genera whose remains are retrieved at each site of excavation. although spectral ordering achieves good performance in the seriation task, it ignores the background knowledge that is naturally present in the domain, as paleontologists can derive the ages of the sites of excavation within some accuracy. on the other hand, the age information is uncertain, so the best approach would be to combine the background knowledge with the information on mammal co-occurrences. motivated by this kind of partial supervision we propose a novel semi-supervised spectral ordering algorithm. our algorithm modifies the laplacian matrix used in spectral ordering, such that domain knowledge of the ordering is taken into account. also, it performs feature selection (sparsification) by discarding features that contribute most to the unwanted variability of the data in bootstrap sampling. the theoretical properties of the proposed algorithm are thoroughly analyzed and it is demonstrated that the proposed framework enhances the stability of the spectral ordering output and induces computational gains.
computational discovery of motifs using hierarchical clustering techniques. discovery of motifs plays a key role in understanding gene regulation in organisms. existing tools for motif discovery demonstrate some weaknesses in dealing with reliability and scalability. therefore, development of advanced algorithms for resolving this problem will be useful. this paper aims to develop data mining techniques for discovering motifs. a mismatch based hierarchical clustering algorithm is proposed in this paper, where three heuristic rules for classifying clusters and a post-processing for ranking and refining the clusters are employed in the algorithm. our algorithm is evaluated using two sets of dna sequences with comparisons. results demonstrate that the proposed techniques in this paper outperform meme, alignace and sombrero for most of the testing datasets.
paired learners for concept drift. to cope with concept drift, we paired a stable online learner with a reactive one. a stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. the method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, since the stable learner performs worse than does there active learner when the target concept changes. while the method uses the reactive learner as an indicator of drift, it uses the stable learner to predict, since the stable learner performs better than does the reactive learner when acquiring target concept. experimental results support these assertions. we evaluated the method by making direct comparisons to dynamic weighted majority, accuracy weighted ensemble, and streaming ensemble algorithm (sea) using two synthetic problems, the stagger concepts and the sea concepts, and three real-world data sets: meeting scheduling, electricity prediction, and malware detection. results suggest that, on these problems, paired learners outperformed or performed comparably to methods more costly in time and space.
inference analysis in privacy-preserving data re-publishing. privacy-preserving data re-publishing (ppdr) deals with publishing microdata in dynamic scenarios. due to privacy concerns, data must be disguised before being published. research in privacy-preserving data publishing (ppdp) has proposed many such methods on static data. in ppdr, multiple appeared records can be used to infer private information of other records. therefore, inference channels exist among different releases. to understand the privacy property of data re-publishing, we need to analyze the impact of these inference channels. previous studies show such analysis when data are updated or disguised in special ways, however, no general method has been proposed. using the maximum entropy modeling method, we have developed a general solution. our method can conduct inference analysis when data are arbitrarily updated or arbitrarily disguised using either generalization or bucketization, two most common data disguise methods in ppdr. through analysis and experiments, we demonstrate the advantage and the effectiveness of our method.
sparcl: efficient and effective shape-based clustering. clustering is one of the fundamental data mining tasks. many different clustering paradigms have been developed over the years, which include partitional, hierarchical, mixture model based, density-based, spectral, subspace, and so on. the focus of this paper is on full-dimensional, arbitrary shaped clusters. existing methods for this problem suffer either in terms of the memory or time complexity (quadratic or even cubic). this shortcoming has restricted these algorithms to datasets of moderate sizes. in this paper we propose sparcl, a simple and scalable algorithm for finding clusters with arbitrary shapes and sizes, and it has linear space and time complexity. sparcl consists of two stages -- the first stage runs a carefully initialized version of the kmeans algorithm to generate many small seed clusters. the second stage iteratively merges the generated clusters to obtain the final shape-based clusters. experiments were conducted on a variety of datasets to highlight the effectiveness, efficiency, and scalability of our approach. on the large datasets sparcl is an order of magnitude faster than the best existing approaches.
classifying high-dimensional text and web data using very short patterns. in this paper, we propose the "democratic classifier", a simple pattern-based classification algorithm that uses very short patterns for classification, and does not rely on the minimum support threshold. borrowing ideas from democracy, our training phase allows each training instance to vote for an equal number of candidate size-2 patterns. the training instances select patterns by effectively balancing between local, class, and global significance of patterns. the selected patterns are simultaneously added to the model for all applicable classes and a novel power law based weighing scheme adjusts their weights with respect of each class. results of experiments performed on 121 common text and web datasets show that our algorithm almost always outperforms state of the art classification algorithms, without any parameter tuning. on 100 real-life web datasets, the average absolute classification accuracy improvement was as great as 9.4% over svm, harmony, c4.5 and knn. also, our algorithm ran about 3.5 times faster than the fastest existing pattern-based classification algorithm.
sparse maximum margin logistic regression for credit scoring. the objective of credit scoring model is to categorizethe applicants as either accepted or rejected debtors prior to granting credit. a modified logistic loss function is proposed which can approximate hinge loss and therefore the resulting model, maximum margin logistic regression (mmlr), has the classification capability of support vector machine (svm) with low computational cost. finally, to classify credit applicants, an efficient algorithm is also described for mmlr based on epsilon-boosting which can provide sparse estimation of coefficients for better stability and interpretability.
a non-parametric approach to pair-wise dynamic topic correlation detection. we introduce dynamic correlated topic models (dctm) for analyzing discrete data over time. this model is inspired by the hierarchical gaussian process latent variable models (gp-lvm). dctm is essentially a non-linear dimension reduction technique which is capable of (1) detecting topic evolution within a document corpus,(2) discovering topic correlations between document corpora, and (3) monitoring topic and correlation trends dynamically. unlike generative aspect models such like lda, dctm demonstrates a much faster converging rate with better model fitting to the data. we empirically assess our approach using 268,231 scientific documents, from the year 1988 to 2005. posterior inferences suggest that dctm is useful for capturing topic and correlation dynamics, as well as predicting their trends.
multi-space-mapped svms for multi-class classification. in svms-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. this is even worse if the number of classes is very large. as a result, the classification accuracy is not as good as expected. in order to improve the performance of svms-based multi-classifiers, this paper proposes a method, named multi-space-mapped svms, to map the classes into different feature spaces and then classify them. the proposed method reduces the requirements for the kernel function. substantial experiments have been conducted on one-against-all, one-against-one, fsvm, ddag algorithms and our algorithm using six uci data sets. the statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.
deck: detecting events from web click-through data. in the past few years there has been increased research interest in detecting previously unidentified events from web resources. our focus in this paper is to detect events from the click-through data generated by web search engines. existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a web search engine corresponds to an event in the real world. in this paper, we address this problem by proposing an effective algorithm which detects events from click-through data deck. we firstly transform click-through data to the 2d polar space by considering the semantic dimension and temporal dimension of queries. robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. finally, events are detected from interesting subspaces using a nonparametric clustering technique. compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
rbnbc: repeat based naive bayes classifier for biological sequences. in this paper, we present rbnbc, a repeat based naive bayes classifier of bio-sequences that uses maximal frequent subsequences as features. rbnbc's design is based on generic ideas that can apply to other domains where the data is organized as collections of sequences. specifically, rbnbc uses a novel formulation of naive bayes that incorporates repeated occurrences of subsequences within each sequence. our extensive experiments on two collections of protein families show that it performs as well as existing state-of-the-art probabilistic classifiers for bio-sequences. this is surprising as it is a pure data mining based generic classifier that does not require domain-specific background knowledge. we note that domain-specific ideas could further increase its performance.
nonnegative matrix factorization for combinatorial optimization: spectral clustering, graph matching, and clique finding. nonnegative matrix factorization (nmf) is a versatile model for data clustering. in this paper, we propose several nmf inspired algorithms to solve different data mining problems. they include (1) multi-way normalized cut spectral clustering, (2) graph matching of both undirected and directed graphs, and (3) maximal clique finding on both graphs and bipartite graphs. key features of these algorithms are (a) they are extremely simple to implement; and (b) they are provably convergent. we conduct experiments to demonstrate the effectiveness of these new algorithms. we also derive a new spectral bound for the size of maximal edge bicliques as a byproduct of our approach.
learning bayesian networks: a map criterion for joint selection of model structure and parameter. for learning bayesian network (bn) structures, it has become common practice to use the bayesian dirichlet (bd) scoring criterion. in contrast to most other scoring metrics that functionally can be interpreted as regularized maximum likelihood criteria, the bd metric cannot be considered as such. the functional dissimilarity of the bd metric compared to other metrics is an obstacle from an analytical point of view; this is for instance becomes clear in the context of the structural em algorithm for learning bns from incomplete data. also, it is not easy to pin-point why exactly and to what extend regularization is taken care of by applying the bd metric. we introduce a bayesian scoring criterion that is closely related to the bd metric, but solves the obvious disadvantages of the bd metric. we arrive at this result by using the same basic assumptions as for the bd metric, but in contrast to the bd metric, where focus is on learning the model structure only, we aim at learning the most probable bn pair jointly, i.e., model structure and the parameter are selected as a pair. this approach yields a scoring metric that has the functional form of a regularized maximum likelihood metric. we perform experiments, and show that this map bn metric also yields better results than the bic and bd metrics on independent test data.
transductive component analysis. in this paper, we study semi-supervised linear dimensionality reduction. beyond conventional supervised methods which merely consider labeled instances, the semi-supervised scheme allows to leverage abundant and ample unlabeled instances into learning so as to achieve better generalization performance. under semi-supervised settings, our objective is to learn a smooth as well as discriminative subspace and linear dimensionality reduction is thus achieved by mapping all samples into the subspace. specifically, we present the transductive component analysis (tca) algorithm to generate such a subspace founded on a graph-theoretic framework.considering tca is non-orthogonal, we further present the orthogonal transductive component analysis (otca) algorithm to iteratively produce a series of orthogonal basis vectors. otca has better discriminating power than tca. experiments carried out on synthetic and real-world datasets by otca show a clear improvement over the results of representative dimensionality reduction algorithms.