Fuzzy distance-based filter-wrapper incremental algorithms for attribute reduction when adding or deleting attribute set

Abstract

Attribute reduction is a critical problem in the data preprocessing step with the aim of minimizing redundant attributes to improve the efficiency of data mining models. The fuzzy rough set theory is considered an effective tool to solve the attribute reduction problem directly on the original decision system, without data preprocessing. With the current digital transformation trend, decision systems are larger in size and updated. To solve the attribute reduction problem directly on change decision systems, a number of recent studies have proposed incremental algorithms to find reducts according to fuzzy rough set approach to reduce execution time. However, the proposed algorithms follow the traditional filter approach. Therefore, the obtained reduct is not optimal in both criteria: the number of attribute of the reducts and the accuracy of classification model. In this paper, we propose incremental algorithms that find reducts following filter-wrapper approach using fuzzy distance measure in the case of adding and deleting attribute set. The experimental results on the sample datasets show that the proposed algorithms significantly reduce the number of attributes in reduct and improve the classification accuracy compared to other algorithms using filter approach