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155
SMOTE: Synthetic Minority Oversampling Technique
 Journal of Artificial Intelligence Research
, 2002
"... An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small percentag ..."
Abstract

Cited by 634 (27 self)
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sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
The Relationship Between PrecisionRecall and ROC Curves
 In ICML ’06: Proceedings of the 23rd international conference on Machine learning
, 2006
"... Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, PrecisionRecall (PR) curves give a more informative picture of an algorithm’s performance. We show that a deep conn ..."
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Cited by 415 (4 self)
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connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve
PAV and the ROC Convex Hull
, 2007
"... Classifier calibration is the process of converting classifier scores into reliable probability estimates. Recently, a calibration technique based on isotonic regression has gained attention within machine learning as a flexible and effective way to calibrate classifiers. We show that, surprisingly ..."
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Cited by 6 (0 self)
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, surprisingly, isotonic regression based calibration using the Pool Adjacent Violators algorithm is equivalent to the ROC convex hull method.
Robust Classification for Imprecise Environments
, 1989
"... In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclas ..."
Abstract

Cited by 341 (15 self)
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and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In:
 3rd International Conference on Knowledge Discovery and Data Mining,
, 1997
"... Abstract Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust t ..."
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Cited by 313 (15 self)
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to imprecise class distributions and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and I,LT..L11~. ...,I.,f ~I! aaapss r;nem 50 cne parr;iculars 01 analyzing iearned classifiers. The method
ROCCER: A ROC convex hull rule learning algorithm
 Proceedings of the ECML/PKDD Workshop on Advances in Inductive Rule Learning
, 2004
"... In this paper we propose a method to construct rule sets that have a convex hull in ROC space. We introduce a rule selection algorithm called ROCCER, which operates by selecting rules from a larger set of rules in order to optimise Area Under the ROC Curve (AUC). Compared with set covering algor ..."
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Cited by 4 (0 self)
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In this paper we propose a method to construct rule sets that have a convex hull in ROC space. We introduce a rule selection algorithm called ROCCER, which operates by selecting rules from a larger set of rules in order to optimise Area Under the ROC Curve (AUC). Compared with set covering
Convex HullBased Multiobjective Genetic Programming for Maximizing ROC Performance
, 2013
"... Receiver operating characteristic (ROC) is usually used to analyse the performance of classifiers in data mining. An important ROC analysis topic is ROC convex hull(ROCCH), which is the least convex majorant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifie ..."
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Cited by 1 (1 self)
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Receiver operating characteristic (ROC) is usually used to analyse the performance of classifiers in data mining. An important ROC analysis topic is ROC convex hull(ROCCH), which is the least convex majorant (LCM) of the empirical ROC curve, and covers potential optima for the given set
CostSensitive Classifier Selection Using the ROC Convex Hull Method
"... One binary classifier may be preferred to another based on the fact that it has better prediction accuracy than its competitor. Without additional information describing the cost of a misclassification, accuracy alone as a selection criterion may not be a sufficiently robust measure when the distrib ..."
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Cited by 1 (0 self)
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information in its formulation. Provost and Fawcett [5, 7] have developed the ROC Convex Hull (ROCCH) method that incorporates techniques from ROC curve analysis, decision analysis, and computational geometry in the search for the optimal classifier that is robust with respect to skewed or imprecise class
A BRANCHANDCUT ALGORITHM FOR THE RESOLUTION OF LARGESCALE SYMMETRIC TRAVELING SALESMAN PROBLEMS
, 1991
"... An algorithm is described for solving largescale instances of the Symmetric Traveling Salesman Problem (STSP) to optimality. The core of the algorithm is a "polyhedral" cuttingplane procedure that exploits a subset of the system of linear inequalities defining the convex hull of the in ..."
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Cited by 205 (7 self)
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An algorithm is described for solving largescale instances of the Symmetric Traveling Salesman Problem (STSP) to optimality. The core of the algorithm is a "polyhedral" cuttingplane procedure that exploits a subset of the system of linear inequalities defining the convex hull
Learning rankings via convex hull separation
 In Advances in Neural Information Processing Systems 18
, 2006
"... We propose efficient algorithms for learning ranking functions from order constraints between sets—i.e. classes—of training samples. Our algorithms may be used for maximizing the generalized Wilcoxon Mann Whitney statistic that accounts for the partial ordering of the classes: special cases include ..."
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Cited by 14 (1 self)
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maximizing the area under the ROC curve for binary classification and its generalization for ordinal regression. Experiments on public benchmarks indicate that: (a) the proposed algorithm is at least as accurate as the current stateoftheart; (b) computationally, it is several orders of magnitude faster
Results 1  10
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155