Test-Cost Sensitive Naive Bayes Classification

Abstract

Inductive learning techniques such as the naive Bayes and decision tree algorithms have been extended in the past to handle different types of costs mainly by distinguishing different costs of classification errors. However, it is an equally important issue to consider how to handle the test costs associated with querying the missing values in a test case. When the value of an attribute is missing in a test case, it may or may not be worthwhile to take the effort to obtain its missing value, depending on how much the value results in a potential gain in the classification accuracy. In this paper, we show how to obtain a test-cost sensitive naive Bayes classifier (csNB) by including a test strategy which determines how unknown attributes are selected to perform test on in order to minimize the sum of the mis-classification costs and test costs. We propose and evaluate several potential test strategies including one that allows several tests to be done at once. We empirically evaluate the csNB method, and show that it compares favorably with its decision tree counterpart.