Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem

Abstract

Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy.

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

“…Heuristics offer problem-specific solutions and are suitable for small-sized problems, whereas metaheuristics (MHs) are simple, flexible, derivative-free, repetitive algorithms which guide a subordinate heuristic by an intelligent mechanism [27][28][29]. Out of these, metaheuristics-based scheduling solutions have produced better results than problem-specific heuristics, especially for complex and bigger scheduling problems [12,13,30]. However, MHs generally experience certain deficiencies, e.g., premature convergence, being caught in local optima, lack of diversity, and imbalance between exploration-exploitation phases [31,32].…”