Binary Drone Squadron Optimization Approaches for Feature Selection

Abstract

A great quantity of data is being created these days, which is kept in massive datasets with different irrelevant attributes that are unrelated to the goal notion. Feature selection deals with the selection of most pertinent features that also aid to increase the classification accuracy. The topic of feature selection is viewed as a multi-objective optimization problem with two goals: improving classification accuracy and reducing the number of features used. Drone Squadron Optimization (DSO) is one of the most recent artifactinspired optimization algorithm; having two key components: semi-autonomous drones that hover over a terrain and a command center that manages the drones. In this paper, two binary variants of the DSO are proposed to deal with the feature selection problem. The proposed binary algorithms are applied on 21 different benchmark datasets against five state-of-the-art algorithms, i.e., Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA) and Ant Lion Optimization (ALO). Different assessment indicators are used to assess the diversification and intensification of the optimization algorithms. When compared to current state-of-the-art wrapper-based algorithms, the suggested binary techniques are more efficient in scanning the dimension space and picking the most useful characteristics for categorization tasks, resulting in the lowest classification error rate.

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

“…At the same time, BBWO uses TFs for binary mapping and employs each of these four selection strategies independently for FS in 22 real datasets. [86] presents the binary versions of the Drone Squadron Optimization (DSO. These versions utilize S-shaped and V-shaped TFs to perform binary mapping from a continuous space to a binary one.…”

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,…”

“…The "ionosphere" dataset emerges at the top with 82 mentions [27,29,37,39,40,[42][43][44]46,47,50,54,55,60,62,[64][65][66]68,70,71,[77][78][79][80]82,[84][85][86][87][88][89][90][91][92]94,95,97,100,102,104,[106][107][108][109][111][112][113]115,[118][119]…”

“…"Wine" [23,29,37,39,42,47,54,57,[64][65][66]68,71,[77][78][79]82,84,86,87,[89][90][91][92][93][94][95]97,[102][103][104][106][107][108][109][110][111][112][118][119][120][123][124][125]127,129,130,132,[134][135][136][137]…”