A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification. (January 2023) - Vommi, Amukta Malyada; Battula, Tirumala Krishna
Highlights: A Bi-phase mutation-based Hybrid EO (BMHEO) method is proposed for feature selection. The approach combines the EO method with a Sine operator and a mutation scheme. Effects of two transfer functions (TF) in BMHEO (S-shape and V-shape) are studied. BMHEO combined with the S-shape TF and RF classifier is the best alternative. Comparative evaluation against twenty biomedical datasets and a MED-NODE dataset. Abstract: With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers – KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved. Graphical abstract: Image, graphical abstract