S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

Abstract

The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight-transfer function (S-shaped and V-shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S-Shaped and V-shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2-BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

“…In [25], the AVOA is a currently proposed metaheuristic procedure that replicates the consumption and movement forms of reliable African vultures. However, AVOA is incapable of cracking the separate search space, which stimulates us to improve the dual AVOA for feature selection issues in classification tasks.…”

A multi-strategy enhanced African vultures optimization algorithm for global optimization problems

“…A multi-objective AVOA version is introduced to handle multi-objective industrial engineering problems (Khodadadi et al, 2022). Another two binary versions of AVOA have been proposed to solve the feature selection (FS) problem using two transfer functions (S and V shaped; Balakrishnan et al, 2022). Other work has been done to solve the FS problem and sentiment analysis on movie reviews (Shaddeli et al, 2022).…”

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

“…In this systematic review, six non-parametric statistical tests were detected. (i) Wilcoxon signed-rank test or Wilcoxon test [29,36,48,50,59,78,79,85,88,90,91,95,99,108,110,115,119,121,[124][125][126]130,131,[136][137][138]142,148,151,152,159,165,169,170], (ii) Friedman test [39,57,59,75,77,94,96,107,108,120,128,132,136,140,142,154,…”

“…The relationship between the formulation of the objective function and the chosen metaheuristic offers a lens into the evolving research preferences and trends in feature selection. Figure 30 The majority of articles with a "weighted multi-objective" formulation predominantly employ both "direct binarization" [73,80,82,87,94,95,98,101,104,105,108,112,115,118,119,123,133,134,136,149,150,152,153,158,159,161,169,177] and "binarization with various approaches" [78,89,91,92,96,106,107,110,114,124,129,131,132,138,142,144,146,154,…”

“…It is more exhaustive and aims to find the optimal binarization method for specific scenarios. Cases of this approach are the articles [47,71,78,89,91,92,96,106,107,110,114,124,129,131,132,138,142,144,146,154,165,172,176,179].…”