Correlation Based Feature Selection with Irrelevant Feature Removal

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. A correlation based feature selection algorithm is developed in traditional methods. A feature subset selection research has focused on searching single query feature subset selection. Also irrelevant and redundant features are removed by this correlation based feature selection algorithm to provide accuracy of targeted data. In proposed work, plan to explore different types of correlation measures, with correlation based feature selection of feature space. Using this correlation based feature selection; multiple correlation/query can be used to get target feature subset selection