Test-Cost Sensitive Ensemble of Classifiers Using Reinforcement Learning
Mohammad Mirhashemi, Reza Anvari, Morteza Barari, Nasser Mozayani · Revue d intelligence artificielle · 2020
The use of classification methods in real-world problems has costs that are usually neglected in the early algorithms which cause inefficiencies in practice.One of these costs, which is significant in many cases, is the cost of obtaining feature values for each instance, named Test-Cost.The Ensemble of classifiers as a common and practical classification method, is also considered and used in this perspective.Each classifier needs a number of features to classify the sample; if instead of using all classifiers, the best arrange of classifiers with the aim of minimizing the needed features is found, an effective solution for lowering the test-cost is obtained.In this paper, a method is proposed which uses reinforcement learning to construct such a Classifier Ensemble.The proposed method learns to find the best sequence of classifiers for each sample to minimize the test-cost.Two problems, an easy one and a hard one, are considered for testing the proposed method, in both of which yields very good results.