Sabu M.K, M.E.S College, India

Feature selection is a problem closely related to dimensionality reduction. A commonly used approach in feature selection is ranking the individual features according to some criteria and then search for an optimal feature subset based on an evaluation criterion to test the optimality. The objective of this work is to predict more accurately the presence of Learning Disability (LD) in school-aged children with reduced number of symptoms. For this purpose, a novel hybrid feature selection approach is proposed by integrating a popular Rough Set based feature ranking process with a modified backward feature elimination algorithm. The approach follows a ranking of the symptoms of LD according to their importance in the data domain. Each symptoms significance or priority values reflect its relative importance to predict LD among the various cases. Then by eliminating least significant features one by one and evaluating the feature subset at each stage of the process, an optimal feature subset is generated. The experimental results shows the success of the proposed method in removing redundant attributes efficiently from the LD dataset without sacrificing the classification performance.

Rough Set Theory, Data Mining, Feature Selection, Learning Disability, Reduct.