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Compact Dual Ensembles for Active Learning



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Compact Dual Ensembles for Active Learning Amit Mandvikar1 Huan Liu1 and Hiroshi Motoda2 1 Arizona State University Arizona USA 2 Osaka University Osaka Japan huanliu amitm asu edu and motoda sanken osaka u ac jp Abstract Generic ensemble methods can achieve excellent learning performance but are not good candidates for active learning because of their different design purposes We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning We empirically show using benchmark data sets that 1 to achieve a good stable ensemble the number of classifiers needed in the ensemble varies for different data sets 2 feature selection can be applied for classifier selection from ensembles to construct compact ensembles with high performance Benchmark data sets and a real world application are used to demonstrate the effectiveness of the proposed approach 1 Introduction Active learning is a framework in which the learner has the freedom to select which data points are added to its training set 11 An active learner may begin with a small number of labeled instances carefully select a few additional instances for which it requests labels learn from the result of those requests and then using its newly gained knowledge carefully choose which instances to request next More often than not data in forms of text including emails image multi media are unlabeled yet many supervised learning tasks need to be performed 2 10 in real world applications Active learning can significantly decrease the number of required labeled instances thus greatly reduce expert involvement Ensemble methods are learning algorithms that construct a set of classifiers and then classify new instances by taking a weighted or unweighted vote of their predictions An ensemble often has smaller expected loss or error rate than any of the n individual member classifiers A good ensemble is one whose members are both accurate and diverse 4 This work explores the relationship



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