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Bagging predictors
, 1996
"... Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making ..."
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Cited by 3650 (1 self)
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Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
 Machine Learning,
, 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
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Cited by 707 (2 self)
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Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a NaiveBayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation, reweighting, and combination techniques, affect classification error. We provide a bias and variance decomposition of the error to show how different methods and variants influence these two terms. This allowed us to determine that Bagging reduced variance of unstable methods, while boosting methods (AdaBoost and Arcx4) reduced both the bias and variance of unstable methods but increased the variance for NaiveBayes, which was very stable. We observed that Arcx4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference. Voting variants, some of which are introduced in this paper, include: pruning versus no pruning, use of probabilistic estimates, weight perturbations (Wagging), and backfitting of data. We found that Bagging improves when probabilistic estimates in conjunction with nopruning are used, as well as when the data was backfit. We measure tree sizes and show an interesting positive correlation between the increase in the average tree size in AdaBoost trials and its success in reducing the error. We compare the meansquared error of voting methods to nonvoting methods and show that the voting methods lead to large and significant reductions in the meansquared errors. Practical problems that arise in implementing boosting algorithms are explored, including numerical instabilities and underflows. We use scatterplots that graphically show how AdaBoost reweights instances, emphasizing not only "hard" areas but also outliers and noise.
Bias plus variance decomposition for zeroone loss functions
 In Machine Learning: Proceedings of the Thirteenth International Conference
, 1996
"... We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no ..."
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Cited by 212 (5 self)
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We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was o ered for the more commonly used zeroone (misclassi cation) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition su ers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequencybased estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository. 1
NonConcave Penalized Likelihood with a Diverging Number of Parameters
, 2003
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Error Reduction through Learning Multiple Descriptions
, 1996
"... . Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount ..."
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Cited by 149 (3 self)
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. Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount of error reduction is linked to the "degree to which the descriptions for a class make errors in a correlated manner." We present a precise and novel definition for this notion and use twentynine data sets to show that the amount of observed error reduction is negatively correlated with the degree to which the descriptions make errors in a correlated manner. We empirically show that it is possible to learn descriptions that make less correlated errors in domains in which many ties in the search evaluation measure (e.g. information gain) are experienced during learning. The paper also presents results that help to understand when and why multiple descriptions are a help (irrelevant attribute...
Wrappers For Performance Enhancement And Oblivious Decision Graphs
, 1995
"... In this doctoral dissertation, we study three basic problems in machine learning and two new hypothesis spaces with corresponding learning algorithms. The problems we investigate are: accuracy estimation, feature subset selection, and parameter tuning. The latter two problems are related and are stu ..."
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Cited by 125 (7 self)
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In this doctoral dissertation, we study three basic problems in machine learning and two new hypothesis spaces with corresponding learning algorithms. The problems we investigate are: accuracy estimation, feature subset selection, and parameter tuning. The latter two problems are related and are studied under the wrapper approach. The hypothesis spaces we investigate are: decision tables with a default majority rule (DTMs) and oblivious readonce decision graphs (OODGs).
MetaLearning in Distributed Data Mining Systems: Issues and Approaches
 Advances of Distributed Data Mining
, 2000
"... Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challeng ..."
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Cited by 103 (0 self)
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Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Metalearning is a technique that seeks to compute higherlevel classifiers (or classification models), called metaclassifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes metalearning and presents the JAM system (Java Agents for Metalearning), an agentbased metalearning system for largescale data mining applications. Specifically, it identifies and addresses several important desiderata for distributed data mining systems that stem from their additional complexity co...
Improving Simple Bayes
, 1997
"... The simple Bayesian classifier (SBC), sometimes called NaiveBayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classificat ..."
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Cited by 74 (1 self)
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The simple Bayesian classifier (SBC), sometimes called NaiveBayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification models even when there are clear conditional dependencies. We examine different approaches for handling unknowns and zero counts when estimating probabilities. Large scale experiments on 37 datasets were conducted to determine the effects of these approaches and several interesting insights are given, including a new variant of the Laplace estimator that outperforms other methods for dealing with zero counts. Using the biasvariance decomposition [15, 10], we show that while the SBC has performed well on common benchmark datasets, its accuracy will not scale up as the dataset sizes grow. Even with these limitations in mind, the SBC can serve as an excellenttool for initial exp...
Using Correspondence Analysis to Combine Classifiers
 Machine Learning
, 1998
"... . Several effective methods have been developed recently for improving predictive performance by generating and combining multiple learned models. The general approach is to create a set of learned models either by applying an algorithm repeatedly to different versions of the training data, or by ap ..."
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Cited by 71 (0 self)
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. Several effective methods have been developed recently for improving predictive performance by generating and combining multiple learned models. The general approach is to create a set of learned models either by applying an algorithm repeatedly to different versions of the training data, or by applying different learning algorithms to the same data. The predictions of the models are then combined according to a voting scheme. This paper focuses on the task of combining the predictions of a set of learned models. The method described uses the strategies of stacking and Correspondence Analysis to model the relationship between the learning examples and their classification by a collection of learned models. A nearest neighbor method is then applied within the resulting representation to classify previously unseen examples. The new algorithm does not perform worse than, and frequently performs significantly better than other combining techniques on a suite of data sets. Keywords: Clas...
Minority Report in Fraud Detection: Classification of Skewed Data
 ACM SIGKDD EXPLORATIONS
, 2004
"... This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partiti ..."
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Cited by 64 (0 self)
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This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replacement. Its originality lies in the use of a single metaclassifier (stacking) to choose the best base classifiers, and then combine these base classifiersâ€™ predictions (bagging) to improve cost savings (stackingbagging). Results from a publicly available automobile insurance fraud detection data set demonstrate that stackingbagging performs slightly better than the best performing bagged algorithm, C4.5, and its best classifier, C4.5 (2), in terms of cost savings. Stackingbagging also outperforms the common technique used in industry (BP without both sampling and partitioning). Subsequently, this paper compares the new fraud detection method (metalearning approach) against C4.5 trained using undersampling, oversampling, and SMOTEing without partitioning (sampling approach). Results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. The most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.