RETRACTED: Hybrid Feature Selection Mechanism based High Dimensional Datesets Reduction (Retracted Article)

Dimension reduction is a critical issue in the analysis of high dimensional datasets, because the high dimensionality of data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Here we present a hybrid feature selection mechanism (HFS) which combines both filter and wrapper models for dimension reduction. In the first stage, we use the filter model to rank the features by the Relief algorithm between each feature and each class, and then choose the highest relevant features to the classes with the help of the threshold. In the second stage, we use Shepley value to evaluate the contribution of features to the classification task in the ranked feature subset. Experimental results show obviously that the HFS method can obtains better classification performance than solo Shepley value based or solo Relief algorithm based method. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Organizers of 2011 International Conference on Energy and Environmental Science.