Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Abstract

Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

“…Since the MOA presents an appropriate combination of classical optimization methods such as particle swarm optimization (PSO) [28], genetic algorithm (GA) [29], and firefly algorithm [30]. It is able to provide better performance in cases of small and large-scale feature sets [31]. In MOA, the position of individuals can be updated based on their current positions ( ) and velocity ( ) in Eq.…”

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

“…Similarly, in a work by Sarkar et al [ 47 ], HS was used for microstructural image classification. Several works [ 48 , 49 ] also exist that have hybridized HS along with other optimization algorithms. This highlights the utility of HS as a competent feature selection algorithm.…”

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

“…That is why researchers have developed different optimization algorithms in different domains to deal with redundant features and it can enhance both exploration and exploitation capability. Some famous and most recent hybrid FS algorithms proposed during recent times are, as follows: Binary Bat Algorithm with Late Acceptance Hill-Climbing (BBA-LAHC) [ 38 ], hybridization of Mayfly algorithm (MA), and HS, named as the MA-HS algorithm [ 39 ], cooperative Genetic Algorithm (CGA) [ 40 ], hybridization of GA with PSO and Ant Colony Optimization (ACO) algorithm [ 41 ], hybrid golden ratio optimization and equilibrium (GREO) [ 42 ], and clustering-based equilibrium and ant colony optimization (EOAS) [ 42 ].…”

“…Hybrid FS models are quite famous among the researchers, as it focuses on both exploration and exploitation. There are a lot of hybrid FS models available in the literature, such as Electrical Harmony based hybrid meta-heuristic (EHHM) [ 46 ], Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm [ 47 ], Mayfly in Harmony [ 39 ], and Binary Social Mimic Optimization Algorithm with X-Shaped Transfer Function [ 48 ]. Successful applications of FS algorithms in various domains have motivated us to propose a new FS algorithm for COVID-19 detection.…”