Feature Selection via Global Redundancy Minimization

Feature Selection via Global Redundancy Minimization

Abstract— Feature Selection via Global Redundancy Minimization. Feature selection has been an important research topic in data mining, because the real data sets often have high dimensional features, such as the bioinformatics and text mining applications. Many existing filter feature selection methods rank features by optimizing certain feature ranking criterions, such that correlated features often have similar rankings. These correlated features are < Final Year Projects 2016 > and don’t provide large mutual information to help data mining. Thus, when we select a limit number of features, we hope to select the top non-redundant features such that the useful mutual information can be maximized. In previous research, Ding et al. recognized this important issue and proposed the m RMR (minimum Redundancy Maximum Relevance Feature Selection) model to minimize the redundancy between sequentially selected features.