  Feature selection has attracted significant attention in data mining and
machine learning in the past decades. Many existing feature selection methods
eliminate redundancy by measuring pairwise inter-correlation of features,
whereas the complementariness of features and higher inter-correlation among
more than two features are ignored. In this study, a modification item
concerning the complementariness of features is introduced in the evaluation
criterion of features. Additionally, in order to identify the interference
effect of already-selected False Positives (FPs), the
redundancy-complementariness dispersion is also taken into account to adjust
the measurement of pairwise inter-correlation of features. To illustrate the
effectiveness of proposed method, classification experiments are applied with
four frequently used classifiers on ten datasets. Classification results verify
the superiority of proposed method compared with five representative feature
selection methods.
