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29
ROC Graphs: Notes and Practical Considerations for Researchers
, 2004
"... Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communitie ..."
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Cited by 225 (1 self)
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Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research.
ROC graphs: Notes and practical considerations for data mining researchers
, 2003
"... Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communitie ..."
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Cited by 158 (0 self)
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Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research. Keywords: 1
Tree Induction for Probabilitybased Ranking
, 2002
"... Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., c ..."
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Cited by 131 (4 self)
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Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probabilitybased rankings, and by how much. In this paper we first discuss why the decisiontree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decisiontree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reducederror pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straghtforward methods for improving probabilitybased rankings. We show that using a simple, common smoothing methodthe Laplace correctionuniformly improves probabilitybased rankings. In addition, bagging substantioJly improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on classmembership probability are required.
Learning relational probability trees
 In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) (2003
"... Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probability estimation trees. Traditional tree learning algorithms assume that instances in the training data are homogenous and i ..."
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Cited by 117 (33 self)
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Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probability estimation trees. Traditional tree learning algorithms assume that instances in the training data are homogenous and independently distributed. Relational probability trees (RPTs) extend standard probability estimation trees to a relational setting in which data instances are heterogeneous and interdependent. Our algorithm for learning the structure and parameters of an RPT searches over a space of relational features that use aggregation functions (e.g. AVERAGE, MODE, COUNT) to dynamically propositionalize relational data and create binary splits within the RPT. Previous work has identified a number of statistical biases due to characteristics of relational data such as autocorrelation and degree disparity. The RPT algorithm uses a novel form of randomization test to adjust for these biases. On a variety of relational learning tasks, RPTs built using randomization tests are significantly smaller than other models and achieve equivalent, or better, performance. 1.
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 111 (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.
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
 In Proceedings of the Eighteenth International Conference on Machine Learning
, 2001
"... Accurate, wellcalibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a costsensitive decision must be made about examples with exampledependent costs. This paper presents simple but successful methods for obtaining calibrated ..."
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Cited by 99 (4 self)
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Accurate, wellcalibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a costsensitive decision must be made about examples with exampledependent costs. This paper presents simple but successful methods for obtaining calibrated probability estimates from decision tree and naive Bayesian classifiers. Using the large and challenging KDD'98 contest dataset as a testbed, we report the results of a detailed experimental comparison of ten methods, according to four evaluation measures. We conclude that binning succeeds in significantly improving naive Bayesian probability estimates, while for improving decision tree probability estimates, we recommend smoothing by estimation and a new variant of pruning that we call curtailment.
Learning and Making Decisions When Costs and Probabilities are Both Unknown
 In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
, 2001
"... In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be ..."
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Cited by 97 (9 self)
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In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be learned. This paper rst discusses how to make optimal decisions given cost and probability estimates, and then presents decision tree learning methods for obtaining wellcalibrated probability estimates. The paper then explains how to obtain unbiased estimators for exampledependent costs, taking into account the diculty that in general, probabilities and costs are not independent random variables, and the training examples for which costs are known are not representative of all examples. The latter problem is called sample selection bias in econometrics. Our solution to it is based on Nobel prizewinning work due to the economist James Heckman. We show that the methods we propose are s...
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.
Tree induction vs. logistic regression: A learningcurve analysis
 CEDER WORKING PAPER #IS0102, STERN SCHOOL OF BUSINESS
, 2001
"... Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership pr ..."
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Cited by 64 (16 self)
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Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learningcurve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about inductionalgorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective atproducing probabilitybased rankings, although apparently comparatively less so foragiven training{set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable canbecharacterized surprisingly well by a simple measure of signaltonoise ratio.
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 61 (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 ...