Test-cost sensitive classification on data with missing values in the limited time

Abstract

Much work [1] [2] has been done to deal with the test-cost sensitive learning on data with missing values. Most of the previous works only focus on the cost while ignore the importance of time. In this paper, we address how to choose the unknown attributes to be tested in the limited time in order to minimize the total cost. We propose a multi-batch strategy applying on test-cost sensitive Naïve Bayes classifier and evaluate its performance on several data sets. We build graphs from attributes and it includes the vertices cost and set cost. Then we use randomized algorithm to select the unknown attributes in each testing cycle. From the results of the experiments, our algorithms significantly outperforms previous algorithms [3][4]. © 2010 Springer-Verlag.