Feature Reduction using a GA-Rough Hybrid Approach on Bio-medical data
ICROS, South Korea
Faculty of Computing, Health and Science
School of Computer and Security Science / Artificial Intelligence and Optimisation Research Centre
In this paper, a new approach is proposed for feature reduction using a GA-Rough hybrid approach on Bio-medical data. The given set of bio-medical data is pre-processed with the min-max normalization method. Then the subsequent evaluation on each feature with respect to the output class is carried out utilizing the information gain-based approach using the entropy-based discretization. Features with zero worth on the evaluated set of features are eliminated. The genetic algorithm is applied for performing a search for most relevant features on the set of features remained. These processes continue until there is no further change on the final reduced set of features. The rough set-based approach is applied on this set of features by applying discernibility matrix-based approach in order to obtain the final reduct. The reduced set of features, or a final reduct, is tested for classification using a TS-type rough-fuzzy classifier to show the viability of the proposed feature reduction approach. The results showed that the proposed feature reduction approach effectively achieved to reduce number of features significantly which reduced to 7 out of 120 features along with compatible classification results on the given bio-medical data compared to other approaches.