Data mining algorithms are originally designed by assuming the data is available at one centralized site. These algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability.
In 2011 designed a hybrid ensemble model for credit risk which combines both clustering and classification @cite_6 . In this SVM classifiers are the members in the ensemble model.
@cite_6
2165721426
Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk assessment. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the application of ensemble in hybrid model leads to better performance of classification. A real dataset is used to test the model performance. The test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods. Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. In this paper, we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models. With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper. In this paper we address the sentence-level multi-modal emotion recognition problem. We formulate the emotion recognition task as a multi-category classification problem and propose an innovative solution based on the automatically generated ensemble of trees with binary support vector machines (SVM) classifiers in the tree nodes. We demonstrate the efficacy of our approach by performing four-way (anger, happiness, sadness, neutral) and five-way (including excitement) emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus using combinations of acoustic features, lexical features extracted from automatic speech recognition (ASR) output and visual features extracted from facial markers traced by a motion capture system. The experiments show that the proposed ensemble of trees of binary SVM classifiers outperforms classical multi-way SVM classification with one-vs-one voting scheme and achieves state-of-the-art results for all feature combinations. An Introduction to Feature Extraction.- An Introduction to Feature Extraction.- Feature Extraction Fundamentals.- Learning Machines.- Assessment Methods.- Filter Methods.- Search Strategies.- Embedded Methods.- Information-Theoretic Methods.- Ensemble Learning.- Fuzzy Neural Networks.- Feature Selection Challenge.- Design and Analysis of the NIPS2003 Challenge.- High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees.- Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems.- Combining SVMs with Various Feature Selection Strategies.- Feature Selection with Transductive Support Vector Machines.- Variable Selection using Correlation and Single Variable Classifier Methods: Applications.- Tree-Based Ensembles with Dynamic Soft Feature Selection.- Sparse, Flexible and Efficient Modeling using L 1 Regularization.- Margin Based Feature Selection and Infogain with Standard Classifiers.- Bayesian Support Vector Machines for Feature Ranking and Selection.- Nonlinear Feature Selection with the Potential Support Vector Machine.- Combining a Filter Method with SVMs.- Feature Selection via Sensitivity Analysis with Direct Kernel PLS.- Information Gain, Correlation and Support Vector Machines.- Mining for Complex Models Comprising Feature Selection and Classification.- Combining Information-Based Supervised and Unsupervised Feature Selection.- An Enhanced Selective Naive Bayes Method with Optimal Discretization.- An Input Variable Importance Definition based on Empirical Data Probability Distribution.- New Perspectives in Feature Extraction.- Spectral Dimensionality Reduction.- Constructing Orthogonal Latent Features for Arbitrary Loss.- Large Margin Principles for Feature Selection.- Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study.- Sequence Motifs: Highly Predictive Features of Protein Function. It is widely recognized that combining multiple classification or regression models typically provides superior results compared to using a single, well-tuned model. However, there are no well known approaches to combining multiple non-hierarchical clusterings. The idea of combining cluster labelings without accessing the original features leads us to a general knowledge reuse framework that we call cluster ensembles. Our contribution in this paper is to formally define the cluster ensemble problem as an optimization problem and to propose three effective and efficient combiners for solving it based on a hypergraph model. Results on synthetic as well as real data sets are given to show that cluster ensembles can (i) improve quality and robustness, and (ii) enable distributed clustering. Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are limited to combining only a finite number of hypotheses, and the generated ensemble is usually sparse. Thus, it is not clear whether we should construct an ensemble classifier with a larger or even an infinite number of hypotheses. In addition, constructing an infinite ensemble itself is a challenging task. In this paper, we formulate an infinite ensemble learning framework based on the support vector machine (SVM). The framework can output an infinite and nonsparse ensemble through embedding infinitely many hypotheses into an SVM kernel. We use the framework to derive two novel kernels, the stump kernel and the perceptron kernel. The stump kernel embodies infinitely many decision stumps, and the perceptron kernel embodies infinitely many perceptrons. We also show that the Laplacian radial basis function kernel embodies infinitely many decision trees, and can thus be explained through infinite ensemble learning. Experimental results show that SVM with these kernels is superior to boosting with the same base hypothesis set. In addition, SVM with the stump kernel or the perceptron kernel performs similarly to SVM with the Gaussian radial basis function kernel, but enjoys the benefit of faster parameter selection. These properties make the novel kernels favorable choices in practice. Some of the most effective recent methods for content-based image classification work by extracting dense or sparse local image descriptors, quantizing them according to a coding rule such as k-means vector quantization, accumulating histograms of the resulting "visual word" codes over the image, and classifying these with a conventional classifier such as an SVM. Large numbers of descriptors and large codebooks are needed for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests - ensembles of randomly created clustering trees - and show that these provide more accurate results, much faster training and testing and good resistance to background clutter in several state-of-the-art image classification tasks. Abstract This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by individual classifier models. A new algorithm based on this perspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is also presented in this paper which shows the practical applicability of the new perspective. Experiments performed on 30 datasets compare KFHE with state-of-the-art multi-class ensemble classification algorithms and show the potential and effectiveness of the new perspective and algorithm. Existing ensemble approaches trade off classification accuracy against robustness to class label noise, but KFHE is shown to be significantly better or at least as good as the state-of-the-art algorithms for datasets both with and without class label noise. This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches. Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. When this order is random, the generalization error typically decreases as the number of classifiers in the ensemble increases. If an appropriate ordering for the aggregation process is devised, the generalization error reaches a minimum at intermediate numbers of classifiers. This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. The performance of these pruned ensembles is evaluated in several benchmark classification tasks under different training conditions. The results of this empirical investigation show that ordered aggregation can be used for the efficient generation of pruned ensembles that are competitive, in terms of performance and robustness of classification, with computationally more costly methods that directly select optimal or near-optimal subensembles. In this paper, we address the task of learning models for predicting structured outputs. We consider both global and local predictions of structured outputs, the former based on a single model that predicts the entire output structure and the latter based on a collection of models, each predicting a component of the output structure. We use ensemble methods and apply them in the context of predicting structured outputs. We propose to build ensemble models consisting of predictive clustering trees, which generalize classification trees: these have been used for predicting different types of structured outputs, both locally and globally. More specifically, we develop methods for learning two types of ensembles (bagging and random forests) of predictive clustering trees for global and local predictions of different types of structured outputs. The types of outputs considered correspond to different predictive modeling tasks: multi-target regression, multi-target classification, and hierarchical multi-label classification. Each of the combinations can be applied both in the context of global prediction (producing a single ensemble) or local prediction (producing a collection of ensembles). We conduct an extensive experimental evaluation across a range of benchmark datasets for each of the three types of structured outputs. We compare ensembles for global and local prediction, as well as single trees for global prediction and tree collections for local prediction, both in terms of predictive performance and in terms of efficiency (running times and model complexity). The results show that both global and local tree ensembles perform better than the single model counterparts in terms of predictive power. Global and local tree ensembles perform equally well, with global ensembles being more efficient and producing smaller models, as well as needing fewer trees in the ensemble to achieve the maximal performance. Despite the successes of distant supervision approaches to relation extraction in the news domain, the lack of a comprehensive ontology of medical relations makes it difficult to apply such approaches to relation classification in the medical domain. In light of this difficulty, we propose an ensemble approach to this task where we exploit human-supplied knowledge to guide the design of members of the ensemble. Results on the 2010 i2b2 VA Challenge corpus show that our ensemble approach yields a 19.8 relative error reduction over a state-of-the-art baseline. Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset. An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier - especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees. Ensemble clustering, as an important extension of the clustering problem, refers to the problem of combining different (input) clusterings of a given dataset to generate a final (consensus) clustering that is a better fit in some sense than existing clusterings. Over the past few years, many ensemble clustering approaches have been developed. However, most of them are designed for partitional clustering methods, and few research efforts have been reported for ensemble hierarchical clustering methods. In this article, a hierarchical ensemble clustering framework that can naturally combine both partitional clustering and hierarchical clustering results is proposed. In addition, a novel method for learning the ultra-metric distance from the aggregated distance matrices and generating final hierarchical clustering with enhanced cluster separation is developed based on the ultra-metric distance for hierarchical clustering. We study three important problems: dendrogram description, dendrogram combination, and dendrogram selection. We develop two approaches for dendrogram selection based on tree distances, and we investigate various dendrogram distances for representing dendrograms. We provide a systematic empirical study of the ensemble hierarchical clustering problem. Experimental results demonstrate the effectiveness of our proposed approaches. Classification with imbalanced data-sets has become one of the most challenging problems in Data Mining. Being one class much more represented than the other produces undesirable effects in both the learning and classification processes, mainly regarding the minority class. Such a problem needs accurate tools to be undertaken; lately, ensembles of classifiers have emerged as a possible solution. Among ensemble proposals, the combination of Bagging and Boosting with preprocessing techniques has proved its ability to enhance the classification of the minority class. In this paper, we develop a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm. Our methodology aims to improve the existing proposals enhancing the performance of the base classifiers by the usage of the evolutionary undersampling approach. Besides, we promote diversity favoring the usage of different subsets of majority class instances to train each base classifier. Centered on two-class highly imbalanced problems, we will prove, supported by the proper statistical analysis, that EUSBoost is able to outperform the state-of-the-art methods based on ensembles. We will also analyze its advantages using kappa-error diagrams, which we adapt to the imbalanced scenario. Ensemble of classifiers, or simply ensemble systems, have been proved to be efficient for pattern recognition tasks. However, its design can become a difficult task. For instance, the choice of its individual classifiers and the use of feature selection methods are very difficult to define in the design of these systems. In order to smooth out this problem, we will apply meta-learning and multi-objective optimization in the choice of important parameters of ensemble systems. Therefore, this work applies meta-learning techniques to define an initial configuration of an multi-objective optimization algorithm, more specifically NSGA II. The meta-learner is used to recommend the proportion of each type of base classifiers to compose the ensemble systems. The NSGA II is used to generate heterogeneous ensembles selecting attributes, types and parameters of base classifiers optimizing the classification error and the bad diversity. The results are analysed using error rate and multi-objective metrics in order to verify whether to use of meta-learning generates more accurate ensemble systems. The main principle of stacked generalization is using a second-level generalizer to combine the outputs of base classifiers in an ensemble. In this paper, after presenting a short survey of the literature on stacked generalization, we propose to use regularized empirical risk minimization (RERM) as a framework for learning the weights of the combiner which generalizes earlier proposals and enables improved learning methods. Our main contribution is using group sparsity for regularization to facilitate classifier selection. In addition, we propose and analyze using the hinge loss instead of the conventional least squares loss. We performed experiments on three different ensemble setups with differing diversities on 13 real-world datasets of various applications. Results show the power of group sparse regularization over the conventional l"1 norm regularization. We are able to reduce the number of selected classifiers of the diverse ensemble without sacrificing accuracy. With the non-diverse ensembles, we even gain accuracy on average by using group sparse regularization. In addition, we show that the hinge loss outperforms the least squares loss which was used in previous studies of stacked generalization. Abstract As one of the most challenging and attractive problems in the pattern recognition and machine intelligence field, imbalanced classification has received a large amount of research attention for many years. In binary classification tasks, one class usually tends to be underrepresented when it consists of far fewer patterns than the other class, which results in undesirable classification results, especially for the minority class. Several techniques, including resampling, boosting and cost-sensitive methods have been proposed to alleviate this problem. Recently, some ensemble methods that focus on combining individual techniques to obtain better performance have been observed to present better classification performance on the minority class. In this paper, we propose a novel ensemble framework called Adaptive Ensemble Undersampling-Boost for imbalanced learning. Our proposal combines the Ensemble of Undersampling (EUS) technique, Real Adaboost, cost-sensitive weight modification, and adaptive boundary decision strategy to build a hybrid algorithm. The superiority of our method over other state-of-the-art ensemble methods is demonstrated by experiments on 18 real world data sets with various data distributions and different imbalance ratios. Given the experimental results and further analysis, our proposal is proven to be a promising alternative that can be applied to various imbalanced classification domains.