Towards an optimal feature subset selection

The problem of feature selection is fundamental in a number of different tasks like classification, data mining, image processing, conceptual learning, etc. In recent times, the growing importance of knowledge discovery and data-mining approaches in practical applications has made the feature selection problem a quite hot topic. Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge. This paper, proposes a new feature subset selection approach by using slicing techniques, which was originally proposed for programming languages. Slicing means that we are interested in automatically obtaining that portion ´features´ of the case responsible for specific parts of the solution of the case at hand. The paper presented the proposed approach based on slicing techniques improved the feature subset selection for classification task in data mining.