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70
Logistic Regression, AdaBoost and Bregman Distances
, 2000
"... We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt al ..."
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Cited by 203 (43 self)
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We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt algorithms designed for one problem to the other. For both problems, we give new algorithms and explain their potential advantages over existing methods. These algorithms can be divided into two types based on whether the parameters are iteratively updated sequentially (one at a time) or in parallel (all at once). We also describe a parameterized family of algorithms which interpolates smoothly between these two extremes. For all of the algorithms, we give convergence proofs using a general formalization of the auxiliaryfunction proof technique. As one of our sequentialupdate algorithms is equivalent to AdaBoost, this provides the first general proof of convergence for AdaBoost. We show that all of our algorithms generalize easily to the multiclass case, and we contrast the new algorithms with iterative scaling. We conclude with a few experimental results with synthetic data that highlight the behavior of the old and newly proposed algorithms in different settings.
Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction
, 2002
"... For large, realworld inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the data and/or the computational costs associated with learning from the data. One question of practical importance is: if n ..."
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Cited by 109 (9 self)
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For large, realworld inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the data and/or the computational costs associated with learning from the data. One question of practical importance is: if n training examples are going to be selected, in what proportion should the classes be represented? In this article we analyze the relationship between the marginal class distribution of training data and the performance of classification trees induced from these data, when the size of the training set is fixed. We study twentysix data sets and, for each, determine the best class distribution for learning. Our results show that, for a fixed number of training examples, it is often possible to obtain improved classifier performance by training with a class distribution other than the naturally occurring class distribution. For example, we show that to build a classifier robust to different misclassification costs, a balanced class distribution generally performs quite well. We also describe and evaluate a budgetsensitive progressivesampling algorithm that selects training examples such that the resulting training set has a good (nearoptimal) class distribution for learning.
The Effect of Class Distribution on Classifier Learning: An Empirical Study
, 2001
"... In this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studie ..."
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Cited by 82 (2 self)
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In this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studies. The first study compares the performance of classifiers generated from unbalanced data sets with the performance of classifiers generated from balanced versions of the same data sets. This comparison allows us to isolate and quantify the effect that the training set's class distribution has on learning and contrast the performance of the classifiers on the minority and majority classes. The second study assesses what distribution is "best" for training, with respect to two performance measures: classification accuracy and the area under the ROC curve (AUC). A tacit assumption behind much research on classifier induction is that the class distribution of the training data should match the "natural" distribution of the data. This study shows that the naturally occurring class distribution often is not best for learning, and often substantially better performance can be obtained by using a different class distribution. Understanding how classifier performance is affected by class distribution can help practitioners to choose training datain realworld situations the number of training examples often must be limited due to computational costs or the costs associated with procuring and preparing the data. 1.
Active Sampling for Class Probability Estimation and Ranking
 Machine Learning
, 2004
"... In many costsensitive environments class probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class probability estimates; however, it often is very costly to obtain training data with class labels ..."
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Cited by 60 (9 self)
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In many costsensitive environments class probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class probability estimates; however, it often is very costly to obtain training data with class labels. Active learning acquires data incrementally, at each phase identifying especially useful additional data for labeling, and can be used to economize on examples needed for learning. We outline the critical features of an active learner and present a samplingbased active learning method for estimating class probabilities and classbased rankings. BOOT STRAPLV identifies particularly informative new data for learning based on the variance in probability estimates, and uses weighted sampling to account for a potential example's informative value for the rest of the input space. We show empirically that the method reduces the number of data items that must be obtained and labeled, across a wide variety of domains. We investigate the contribution of the components of the algorithm and show that each provides valuable information to help identify informative examples. We also compare BOOTSTRAP LV with UNCERTAINTY SAMPLING, an existing active learning method designed to maximize classification accuracy. The results show that BOOTSTRAPLV uses fewer examples to exhibit a certain estimation accuracy and provide insights to the behavior of the algorithms. Finally, we experiment with another new active sampling algorithm drawing from both UNCERTAINTY SAMPLING and BOOTSTRAPLV and show that it is significantly more competitive with BOOTSTRAPLV compared to UNCERTAINTY SAMPLING. The analysis suggests more general implications for improving existing active sampling ...
A Taxonomy of Recommender Agents on the Internet
 ARTIFICIAL INTELLIGENCE REVIEW
, 2003
"... Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in sea ..."
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Cited by 53 (1 self)
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Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in searching, sorting, classifying, filtering and sharing this vast quantity of information. In this paper, we present a stateoftheart taxonomy of intelligent recommender agents on the Internet. We have analyzed 37 different systems and their references and have sorted them into a list of 8 basic dimensions. These dimensions are then used to establish a taxonomy under which the systems analyzed are classified. Finally, we conclude this paper with a crossdimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.
Mining Needles in a Haystack: Classifying Rare Classes via TwoPhase Rule Induction
, 2001
"... Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed twophase rule induction method in the context of learning compl ..."
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Cited by 52 (12 self)
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Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed twophase rule induction method in the context of learning complete and precise signatures of rare classes. The key feature of our method is that it separately conquers the objectives of achieving high recall and high precision for the given target class. The first phase of the method aims for high recall by inducing rules with high support and a reasonable level of accuracy. The second phase then tries to improve the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules. Existing sequential covering techniques try to achieve high precision for each individual disjunct learned. In this paper, we claim that such approach is inadequate for rare classes, because of two problems: splintered false positives and errorprone small disjuncts. Motivated by the strengths of our twophase design, we design various synthetic data models to identify and analyze the situations in which two stateoftheart methods, RIPPER and C4.5rules, either fail to learn a model or learn a very poor model. In all these situations, our twophase approach learns a model with significantly better recall and precision levels. We also present a comparison of the three methods on a challenging reallife network intrusion detection dataset. Our method is significantly better or comparable to the best competitor in terms of achieving better balance between recall and precision. 1.
Subgroup Discovery with CN2SD
 Journal of Machine Learning Research
, 2004
"... discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit ..."
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Cited by 52 (10 self)
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discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental evaluation of CN2SD on 23 UCI data sets shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve, when compared with the CN2 algorithm. Application of CN2SD to a large traffic accident data set confirms these findings.
Theoretical Views of Boosting and Applications
, 1999
"... . Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we briefly survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theo ..."
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Cited by 50 (2 self)
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. Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we briefly survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. Some empirical work and applications are also described. Background Boosting is a general method which attempts to "boost" the accuracy of any given learning algorithm. Kearns and Valiant [29, 30] were the first to pose the question of whether a "weak" learning algorithm which performs just slightly better than random guessing in Valiant's PAC model [44] can be "boosted" into an arbitrarily accurate "strong" learning algorithm. Schapire [36] came up with the first provable polynomialtime boosting algorithm in 1989. A year later, Freund [16] developed a much more effici...
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison And Improvements
, 2001
"... Classification of rare events has many important data mining applications. Boosting is a promising metatechnique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. ..."
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Cited by 45 (4 self)
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Classification of rare events has many important data mining applications. Boosting is a promising metatechnique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. In this paper, we evaluate three existing categories of boosting algorithms from the single viewpoint of how they update the example weights in each iteration, and discuss their possible effect on recall and precision of the rare class. We propose enhanced algorithms in two of the categories, and justify their choice of weight updating parameters theoretically. Using some specially designed synthetic datasets, we compare the capability of all the algorithms from the rare class perspective. The results support our qualitative analysis, and also indicate that our enhancements bring an extra capability for achieving better balance between recall and precision in mining rare classes.