Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Abstract

Background: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers