Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data

• URL: http://arxiv.org/abs/2401.12667v1
• Date: Tue, 23 Jan 2024 11:22:03 GMT
• Title: Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data
• Authors: Zardad Khan, Amjad Ali, Saeed Aldahmani
• Abstract summary: A robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative feature for high dimensional gene expression binary classification with class-imbalance problem. The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative feature for high dimensional gene expression binary classification with class-imbalance problem. The method addresses one of the most challenging problems of highly skewed class distributions in gene expression datasets that adversely affect the performance of classification algorithms. First, the training dataset is balanced by synthetically generating data points from minority class observations. Second, a minimum subset of genes is selected using a greedy search approach. Third, a novel weighted robust score, where the weights are computed by support vectors, is introduced to obtain a refined set of genes. The highest-scoring genes based on this approach are combined with the minimum subset of genes selected by the greedy search approach to form the final set of genes. The novel method ensures the selection of the most discriminative genes, even in the presence of skewed class distribution, thus improving the performance of the classifiers. The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets. Classification accuracy and sensitivity are used as performance metrics to compare the proposed ROWSU algorithm with several other state-of-the-art methods. Boxplots and stability plots are also constructed for a better understanding of the results. The results show that the proposed method outperforms the existing feature selection procedures based on classification performance from k nearest neighbours (kNN) and random forest (RF) classifiers.

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