NSC-GA: Search for optimal shrinkage thresholds for nearest shrunken centroid
Faculty of Health, Engineering and Science
School of Computer and Security Science/Artificial Intelligence and Optimisation Research Group
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative differences between the overall centroid and the class centroid. However, selecting this threshold value via 'trial and error' in empirical approaches can be time-consuming and imprecise. In the proposed NSC-GA approach, shrinkage threshold values for the NSC are encoded as genes in chromosomes that are evaluated using a fitness measure obtained from the classifier in the NSC. The proposed approach automatically searches for the optimal threshold for the NSC by utilizing GA. The proposed approach was evaluated using a number of data sets; Alzheimer's disease, Colon and Leukemia cancer datasets. Experimental results indicated that the proposed approach finds the optimal range of shrinkage thresholds for each dataset, subsequently leading to a higher classification result and involving a smaller number of features when compared to previous studies.