Feature Selection on Inconsistent Data

With the explosive growth of data size, inconsistent data appear more frequently. Due to inconsistent data detection and repairing in data preprocessing, feature selection approaches are lack of efficiency. To avoid this problem, we develop a novel feature selection method on inconsistent data which considers the inconsistency issues into the process of feature selection. This method not only decreases the time costs but also guarantees the accuracy of machine learning models. Extensive evaluation results show the high efficiency and effectiveness of the proposed approach. We introduce the research motivation of this chapter in Sect. 6.1. Mutual information and consistency rules are reviewed in Sect. 6.2. The feature selection method is presented in Sect. 6.3. Extensive experiments and result analyses are discussed in Sect. 6.4. We conclude this chapter in Sect. 6.5.