Feature Selection based on Discernibility Function in Incomplete Data with Fuzzy Decision

摘要

Rough set theory has been applied successfully in knowledge discovery, computational intelligence and decision analysis. It can only deal with features of symbolic type for complete data. However, incomplete data with fuzzy decision under a preference-ordered relation is common in real-world applications. In this paper, we propose a feature selection framework for such data by combining the dominance-based rough sets. At first, the judgment theorems are established by the Boolean reasoning techniques. Then, the discernibility matrix and the discernibility function approach are proposed to find all subsets of features. In addition, an efficient feature selection algorithm to find a feature subset is proposed. Finally, the experimental results show that, in most cases for different data sets, the proposed algorithm is effective and efficient for feature selection from the incomplete data with fuzzy decision.