A Multi-Objective Evolutionary Approach for Preprocessing Imbalanced Microarray Datasets

Abstract

Microarray datasets are usually imbalanced with a huge number of features and few samples. Most of the existing methods consider classification accuracy as the performance measure while converting the imbalanced datasets into a balanced one and selecting the optimal feature subset, resulting in overfitting. To address the above-mentioned issue, in this paper, a multi-objective genetic algorithm approach is proposed that utilizes a combination of evaluation metrics for both imbalanced data processing and feature selection. The proposed methodology is implemented in seven real datasets taken from public repositories. The cross-validation results prove that the proposed methodology outperforms existing methods in all seven datasets. The proposed methodology serves as a simple and efficient method for preprocessing microarray datasets and helps clinical practitioners in making strategic decisions with the help of a few features. It will also greatly reduce the time spent on processing unnecessary features.