Feature selection via neighborhood multi-granulation fusion

Multi-label feature selection based on max-dependency and min-redundancy

“…As we know, feature selection is an effective technique which tries to select an optimal subset of features from the original feature space [5, 22,24]. The process of multi-label feature selection can be divided into two steps.…”

Compacted decision tables based attribute reduction

“…Among existing feature selection algorithms, supervised feature selection algorithms are commonly employed to process the data with class labels, in which there are some representatives, such as feature selection algorithm with feature selection algorithm based on mRMR [32], sparsity-inducing norms [14], feature selection algorithm based on t-test [44,45], feature subset selection algorithm with ordinal optimization [5] and feature selection algorithm based on neighborhood multi-granulation fusion [25]. For the investigation of feature selection, one of critical issues is how to select feature subset, and filters, wrappers and embedded methods have been generally recognized as the most popular methods to solve the issue [2,8].…”

Multigranulation decision-theoretic rough sets in incomplete information systems

“…She and He [23] studied the algebraic and topological structure of multigranulation rough sets. Lin et al [17] studied feature selection via neighborhood multi-granulation fusion. Liu et al [14] and Lin et al [15] generalized multigranulation rough sets based on equivalence relations to rough sets based on covering [25].…”