Feature Selection with Redundancy-complementariness Dispersion
This work addresses feature selection for data mining and machine learning, but it appears incremental as it builds on existing methods by adding modifications rather than introducing a new paradigm.
The paper tackled the problem of feature selection by addressing the limitations of existing methods that ignore complementariness and higher-order correlations among features, introducing a modification item for complementariness and redundancy-complementariness dispersion, and demonstrated its effectiveness through classification experiments on ten datasets, showing superiority over five representative methods.
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.