A Scalable Parallel Gene Selection Method Based on Hybrid Bio-Inspired Metaheuristic Algorithms with Shapley Value Analysis
Keywords:
Hybrid Genetic Dung beetle Optimization (HGDBO), Synthetic Minority Oversampling Technique (SMOTE), Cooperative based Kernel Shapley Values (CkSV).Abstract
The development of microarray technology has made a significant contribution to the prediction of various cancer types and their subtypes through gene selection. Effective hybrid approaches are currently inadequate for the challenging problem of predicting highly discriminative genes in microarrays. Thus, this study proposes a novel approach to selecting parallel gene based on a hybrid bio inspired feature selection method. Initially, microarray data is augmented using the Synthetic Minority Oversampling Technique (SMOTE) to enhance dataset sizes. Then, the Cooperative based Kernel Shapley Values (CkSV) approach is employed to extract features and determine Shapley values. The Hybrid Genetic Dung beetle Optimization (HGDBO) approach was employed to identify the most valuable features. Also, the process is executed on the Apache Hadoop Distributed File System for storing large datasets and cost effectiveness. In addition, the features are classified using several machine learning methods such as Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and K-nearest neighbour. As a result, the proposed approach is compared to other machine learning based classification algorithms. Eleven datasets are used to assess the outcomes of the analysis of the proposed method, which is conducted using the Python tool. The results of the simulations show that the proposed strategy outperforms the existing methods. For SVM classifier, dataset 1 has an accuracy (0.97), dataset 2 (0.98), dataset3 (0.968), dataset 4 (0.975), dataset 5 (0.973), dataset 6 (0.979), dataset 7 (0.985), dataset 8(0.972), dataset 9 (0.973), dataset 10(0.980) and dataset 11(0.979), respectively.
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