Global Best Artificial Bee Colony for Minimal Test Cost Attribute Reduction

Abstract

The minimal test cost attribute reduction is an important component in data mining applications, and plays a key role in cost-sensitive learning. Recently, several algorithms are proposed to address this problem, and can get acceptable results in most cases. However, the effectiveness of the algorithms for large datasets are often unacceptable. In this paper, we propose a global best artificial bee colony algorithm with an improved solution search equation for minimizing the test cost of attribute reduction. The solution search equation introduces a parameter associated with the current global optimal solution to enhance the local search ability. We apply our algorithm to four UCI datasets. The result reveals that the improvement of our algorithm tends to be obvious on most datasets tested. Specifically, the algorithm is effective on large dataset Mushroom. In addition, compared to the information gain-based reduction algorithm and the ant colony optimization algorithm, the results demonstrate that our algorithm has more effectiveness, and is thus more practical.