Optimal Sub-Reducts with Test Cost Constraint

Cost-sensitive learning extends classical machine learning by considering various types of costs, such as test costs and misclassification costs, of the data. In many applications, there is a test cost constraint due to limited money, time, or other resources. It is necessary to deliberately choose a set of tests to preserve more useful information for classification. To cope with this issue, we define optimal sub-reducts with test cost constraint and a corresponding problem for finding them. The new problem is more general than two existing problems, namely the minimal test cost reduct problem and the 0-1 knapsack problem, therefore it is more complex than both of them. We propose two exhaustive algorithms to deal with it. One is straightforward, and the other takes advantage of some properties of the problem. The efficiencies of these two algorithms are compared through experiments on the mushroom dataset. Some potential enhancements are also pointed out.