Pruning Ensembles with Cost Constraints
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The paper presents a cost-sensitive classifier ensemble pruning method, which employs a genetic algorithm to choose the most promising ensemble. In this study the pruning algorithm considers constraints put on the cost of selected features, which is the one of the key-problems in the real-life decision support systems, especially dedicated medical support systems. The proposed method takes into consideration both the overall classification accuracy and the cost constraints, returning balanced solution for the problem at hand. Additionally, also to boost the value of the exploitation cost, we propose to use cost-sensitive decision trees as the base classifiers. The pruning algorithm was evaluated on the basis of the comprehensive computer experiments run on cost-sensitive medical benchmark datasets.
KeywordsPattern classification Machine learning Classifier ensemble Classifier ensemble pruning Cost-sensitive classification
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This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264.
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