Clustering-Based Sequential Feature Selection Approach For High Dimensional Data Classification

Feature selection has become the focus of many research applications specially when datasets tend to be huge. Recently, approaches that use feature clustering techniques have gained much attention for their ability to improve the selection process. In this paper, we propose a clustering-based sequential feature selection approach based on a three step filter model. First, irrelevant features are removed. Then, an automatic feature clustering algorithm is applied in order to divide the feature set into a number of clusters in which features are redundant or correlated. Finally, one feature is sequentially selected per group. Two experiments are conducted, the first one using six real wold numerical data and the second one using features extracted from three color texture image datasets. Compared to seven feature selection algorithms, the obtained results show the effectiveness and the efficiency of our approach.