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Anytime learning of anycost classifiers
"... The classification of new cases using a predictive model incurs two types of costs—testing costs and misclassification costs. Recent research efforts have resulted in several novel algorithms that attempt to produce learners that simultaneously minimize both types. In many real life scenarios, howe ..."
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The classification of new cases using a predictive model incurs two types of costs—testing costs and misclassification costs. Recent research efforts have resulted in several novel algorithms that attempt to produce learners that simultaneously minimize both types. In many real life scenarios, however, we cannot afford to conduct all the tests required by the predictive model. For example, a medical center might have a fixed predetermined budget for diagnosing each patient. For cost bounded classification, decision trees are considered attractive as they measure only the tests along a single path. In this work we present an anytime framework for producing decisiontree based classifiers that can make accurate decisions within a strict bound on testing costs. These bounds can be known to the learner, known to the classifier but not to the learner, or not predetermined. Extensive experiments with a variety of datasets show that our proposed framework produces trees with lower misclassification costs along a wide range of testing cost bounds.
Maximal falsifiability: Definitions, algorithms, and applications
 In: LPAR
, 2013
"... Abstract. Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem ..."
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Abstract. Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem has been characterized in terms of Maximal Satisfiable Subsets (MSSes) and Minimal Correction Subsets (MCSes), as well as Minimal Unsatisfiable Subsets (MUSes) and minimal hitting set dualization. However, and in contrast with MaxSAT, no such characterizations exist for MinSAT. This paper addresses this issue by casting the MinSAT problem in a more general framework. The paper studies Maximal Falsifiability, the problem of computing a subsetmaximal set of clauses that can be simultaneously falsified, and shows that MinSAT corresponds to the complement of a largest subsetmaximal set of simultaneously falsifiable clauses, i.e. the solution of the Maximum Falsifiability (MaxFalse) problem. Additional contributions of the paper include novel algorithms for Maximum and Maximal Falsifiability, as well as minimal hitting set dualization results for the MaxFalse problem. Moreover, the proposed algorithms are validated on practical instances. 1
1Maximal Falsifiability Definitions, Algorithms, and Applications
"... Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem has been ..."
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Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem has been characterized in terms of Maximal Satisfiable Subsets (MSSes) and Minimal Correction Subsets (MCSes), as well as Minimal Unsatisfiable Subsets (MUSes) and minimal hitting set dualization. However, and in contrast with MaxSAT, no such characterizations exist for MinSAT. This paper addresses this issue by casting the MinSAT problem in a more general framework. The paper studies Maximal Falsifiability, the problem of computing a subsetmaximal set of clauses that can be simultaneously falsified, and shows that MinSAT corresponds to the complement of a largest subsetmaximal set of simultaneously falsifiable clauses, i.e. the solution of the Maximum Falsifiability (MaxFalse) problem. Additional contributions of the paper include novel algorithms for Maximum and Maximal Falsifiability, as well as minimal hitting set dualization results for the MaxFalse problem. Moreover, the proposed algorithms are validated on practical instances.
Proceedings of the TwentyFifth AAAI Conference on Artificial Intelligence Towards Maximizing the Area Under the ROC Curve for MultiClass Classification Problems
"... The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution ..."
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The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment. MAUC, the extension of AUC to multiclass problems, has also attracted a lot of attention. However, despite the emergence of approaches for training classifiers with large AUC, little has been done for MAUC. This paper analyzes MAUC indepth, and reveals that the maximization of MAUC can be achieved by decomposing the multiclass problem into a number of independent subproblems. These subproblems are formulated in the form of a “learning to rank ” problem, for which wellestablished methods already exist. Based on the analysis, a method that employs RankBoost algorithm as the subproblem solver is proposed to achieve classification systems with maximum MAUC. Empirical studies have shown the advantages of the proposed method over other eight relevant methods. Due to the importance of MAUC to multiclass costsensitive learning and class imbalanced learning problems, the proposed method is a general technique for both problems. It can also be generalized to accommodate other learning algorithms as the subproblem solvers.
1Minimax Classifier for Uncertain Costs
"... Abstract—Many studies on the costsensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many realworld problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different co ..."
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Abstract—Many studies on the costsensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many realworld problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motivated by these facts, this study aims to seek the minimax classifier over multiple cost matrices. In summary, we theoretically proved that, no matter how many cost matrices are involved, the minimax problem can be tackled by solving a number of standard costsensitive problems and subproblems that involve only two cost matrices. As a result, a general framework for achieving minimax classifier over multiple cost matrices is suggested and justified by preliminary empirical studies. I.