IPCM separability ratio for supervised feature selection

Collecting data is very easy now owing to fast computers and ease of Internet access. It raises the problem of the curse of dimensionality to supervised classification problems. In our previous work, an Intra-Prototype / Inter-Class Separability Ratio (IPICSR) model is proposed to select relevant features for semi-supervised classification problems. In this work, a new margin based feature selection model is proposed based on the IPICSR model for supervised classification problems. Owing to the nature of supervised classification problems, a more accurate class separating margin could be found by the classifier. We adopt this advantage in the new Intra-Prototype / Class Margin Separability Ratio (IPCMSR) model. Experimental results are promising when compared to several existing methods using 4 UCI datasets.