FFS-MCC: Fusing approximation and fuzzy uncertainty measures for feature selection with multi-correlation collaboration

In many practical applications, the characteristics of data collected from multiple sources are high-dimensional, uncertain, and complexly correlated, which brings great challenges to feature selection. It is much difficult to select suitable features quickly and accurately for high-dimensional and uncertain (usually randomness, fuzziness, and inconsistency) data. Correlations between features are complex and their collaborations are dynamically changing in an uncertain data environment. Since the classification accuracy and the reduction rate of feature selection are conflicting, it is hard to obtain a trade-off between them. For the issues mentioned above, in this work, a feature selection method with multiple correlations and their collaborations is proposed by fusing the approximation and fuzzy uncertainty measures. Specifically, the feature-class correlation is defined by the approximation uncertainty measure while feature-feature correlations are mined by the fuzzy uncertainty measure. Further, a collaborative intensity is calculated by positive or negative effect of multiple correlations. A novel feature evaluation strategy of max-dependent relevance and min-conditional redundancy based on the multi-correlation collaborative intensity is proposed. Experimental results show that the proposed algorithm on joint evaluation outperforms the compared ones because of the considered multi-correlation collaboration between features.