Accelerating incomplete feature selection
Yuhua Qian, Jiye Liang, Wei Wei · 2009
Feature selection from incomplete data aims to retain the discriminatory power of original features in rough set theory. Many feature selection algorithms are computationally time-consuming. To overcome this drawback, we introduce a theoretic framework based on rough set theory, called positive approximation, which can be used to accelerate a heuristic process of feature selection from incomplete data. Based on the proposed accelerator, a general feature selection algorithm is designed. Through the use of the accelerator, several representative heuristic feature selection algorithms in rough set theory have been enhanced. Experiments show that these modified algorithms outperform their original counterparts.