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

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