Attribute Selection via Maximizing Independent-and-Effective Classification Information Ratio

Attribute selection in rough set theory has wide practical application values. Most existing attribute selection approaches neglect the relationship among the classification information and redundant information brought by the candidate attribute, and the retained classification information provided by the selected attributes when selecting the candidate attribute. Therefore, the significant evaluation function of effective classification information ratio is defined for attribute selection, and an attribute selection approach via the effective classification information ratio is proposed further, which can effectively select the attributes that can provide lots of effective classification information and low redundant information. Besides, considering the influence of candidate attribute on the retained classification information provided by the selected attributes, another significant evaluation function of independent-and-effective classification information ratio is advanced, and an improved attribute selection approach is proposed, which can contribute to balancing the relationship between the effective classification information and redundant information of the attributes, and improving the overall recognition ability of the selected attribute subset. Finally, comparative experiments are conducted from the aspects of classification performance and statistical Bonferroni-Dunn test, and the experimental results illustrate that the proposed attribute selection approaches are effective.