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114
Error Correlation And Error Reduction In Ensemble Classifiers
, 1996
"... Using an ensemble of classifiers, instead of a single classifier, can lead to improved generalization. The gains obtained by combining however, are often affected more by the selection of what is presented to the combiner, than by the actual combining method that is chosen. In this paper we focus ..."
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Cited by 181 (24 self)
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Using an ensemble of classifiers, instead of a single classifier, can lead to improved generalization. The gains obtained by combining however, are often affected more by the selection of what is presented to the combiner, than by the actual combining method that is chosen. In this paper we focus on data selection and classifier training methods, in order to "prepare" classifiers for combining. We review a combining framework for classification problems that quantifies the need for reducing the correlation among individual classifiers. Then, we discuss several methods that make the classifiers in an ensemble more complementary. Experimental results are provided to illustrate the benefits and pitfalls of reducing the correlation among classifiers, especially when the training data is in limited supply. 2 1 Introduction A classifier's ability to meaningfully respond to novel patterns, or generalize, is perhaps its most important property (Levin et al., 1990; Wolpert, 1990). In...
Neural networks for classification: a survey
 and Cybernetics  Part C: Applications and Reviews
, 2000
"... Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability esti ..."
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Cited by 132 (0 self)
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Abstract—Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics. Index Terms—Bayesian classifier, classification, ensemble methods, feature variable selection, learning and generalization, misclassification costs, neural networks. I.
Combining Multiple Classifiers By Averaging Or By Multiplying?
, 2000
"... In classification tasks it may be wise to combine observations from di!erent sources. Not only it decreases the training time but it can also increase the robustness and the performance of the classi"cation. Combining is often done by just (weighted) averaging of the outputs of the di!erent cla ..."
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Cited by 108 (4 self)
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In classification tasks it may be wise to combine observations from di!erent sources. Not only it decreases the training time but it can also increase the robustness and the performance of the classi"cation. Combining is often done by just (weighted) averaging of the outputs of the di!erent classi"ers. Using equal weights for all classi"ers then results in the mean combination rule. This works very well in practice, but the combination strategy lacks a fundamental basis as it cannot readily be derived from the joint probabilities. This contrasts with the product combination rule which can be obtained from the joint probability under the assumption of independency. In this paper we will show di!erences and similarities between this mean combination rule and the product combination rule in theory and in practice. # 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Linear and Order Statistics Combiners for Pattern Classification
 Combining Artificial Neural Nets
, 1999
"... Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification resul ..."
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Cited by 74 (8 self)
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Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the "added" error. If N unbiased classifiers are combined by simple averaging, the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based nonlinear combiners, we derive expressions that indicate how much the median, the maximum and in general the ith order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.
Parallel consensual neural networks
 MULTIPLE CLASSIFIERS APPLIED TO MULTISOURCE REMOTE SENSING DATA 2299
, 1997
"... Abstract — A new type of a neuralnetwork architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage ne ..."
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Cited by 51 (6 self)
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Abstract — A new type of a neuralnetwork architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugategradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data. Index Terms — Consensus theory, wavelet packets, accuracy, classification, probability density estimation, statistical pattern
A Theory of Multiple Classifier Systems And Its Application to Visual Word Recognition
, 1992
"... Despite the success of many pattern recognition systems in constrained domains, problems that involve noisy input and many classes remain difficult. A promising direction is to use several classifiers simultaneously, such that they can complement each other in correctness. This thesis is concerned w ..."
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Cited by 34 (8 self)
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Despite the success of many pattern recognition systems in constrained domains, problems that involve noisy input and many classes remain difficult. A promising direction is to use several classifiers simultaneously, such that they can complement each other in correctness. This thesis is concerned with decision combination in a multiple classifier system that is critical to its success. A multiple classifier system consists of a set of classifiers and a decision combination function. It is a preferred solution to a complex recognition problem because it allows simultaneous use of feature descriptors of many types, corresponding measures of similarity, and many classification procedures. It also allows dynamic selection, so that classifiers adapted to inputs of a particular type may be applied only when those inputs are encountered. Decisions by the classifiers are represented as rankings of the class set that are derivable from the results of feature matching. Rank scores contain more ...
A Global Optimization Technique for Statistical Classifier Design
 IEEE Transactions on Signal Processing
"... A global optimization method is introduced for the design of statistical classifiers that minimize the rate of misclassification. We first derive the theoretical basis for the method, based on which we develop a novel design algorithm and demonstrate its effectiveness and superior performance in the ..."
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Cited by 28 (10 self)
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A global optimization method is introduced for the design of statistical classifiers that minimize the rate of misclassification. We first derive the theoretical basis for the method, based on which we develop a novel design algorithm and demonstrate its effectiveness and superior performance in the design of practical classifiers for some of the most popular structures currently in use. The method, grounded in ideas from statistical physics and information theory, extends the deterministic annealing approach for optimization, both to incorporate structural constraints on data assignments to classes and to minimize the probability of error as the cost objective. During the design, data are assigned to classes in probability, so as to minimize the expected classification error given a specified level of randomness, as measured by Shannon's entropy. The constrained optimization is equivalent to a free energy minimization, motivating a deterministic annealing approach in which the entropy...
Locating Facial Features in Image Sequences using Neural Networks
 In 2 nd International Conference on Automatic Face and Gesture Recognition
, 1997
"... This paper describes a method for the automatic location of facial features, such as eyes, nose and mouth, in image sequences using a neural network approach. It is shown that by modeling the feature sought as a structural assembly of microfeatures, and by using a probabilistic interpretation of ne ..."
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Cited by 27 (0 self)
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This paper describes a method for the automatic location of facial features, such as eyes, nose and mouth, in image sequences using a neural network approach. It is shown that by modeling the feature sought as a structural assembly of microfeatures, and by using a probabilistic interpretation of neural network outputs, it is possible to construct a location system that is more robust than a location system which uses the feature as a single entity. With this microfeature approach, not only the position of the features can be found, but also the shape of the features. 1. Introduction From research areas like face recognition and modelbased coding, there is growing attention for locating eyes automatically in image sequences [2, 3]. This paper proposes a method using neural networks and, as a special case, it presents a newly developed advanced eye detector. By interpreting the neural network outputs as a probability, and by using a probabilistic method to model prior knowledge about t...
Classifier Ensembles: Select RealWorld Applications
, 2008
"... Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the p ..."
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Cited by 23 (0 self)
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Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can be alleviated is by using ensembles of classifiers, where a variety of classifiers (either different types of classifiers or different instantiations of the same classifier) are pooled before a final classification decision is made. Intuitively, classifier ensembles allow the different needs of a difficult problem to be handled by classifiers suited to those particular needs. Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we survey select applications of ensemble methods to problems that have historically been most representative of the difficulties in classification. In particular, we survey applications of ensemble methods to remote sensing, person recognition, one vs. all recognition, and medicine.
Robust Combining of Disparate Classifiers through Order Statistics
 Pattern Analysis and Applications
, 2001
"... Integrating the outputs of multiple classifiers via combiners or metalearners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are larg ..."
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Cited by 22 (5 self)
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Integrating the outputs of multiple classifiers via combiners or metalearners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the i th order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.