Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

Table 1

A brief survey on feature selection models.

Reference	Classifier	Metaheuristic	No. of features	Fixed subset size	Domain
Talbi et al. [2]	SVM	PSO, GA	N/A	N/A	Gene microarray
Vieira et al. [3]	SVM	BPSO, GA	12, 28	No	SEPSIS data
Wang et al. [4]	RS	SS	14, 15	No	Credit scoring
Abd-Alsabour and Moneim [5]	SVM	ACO	17–70	No	General
Casado et al. [6]	DA	TS	54–121	Yes (5–8)	General
Jona and Nagaveni [7]	SVM	ACO, Cuckoo	78	Yes (5)	Mammogram
Unler et al. [8]	SVM	PSO	10–267	Min	General
Korycinski et al. [9]	BHC	TS	242	Yes (3–8)	Hyperspectral
Yusta [10]	N/A	GRASP, TS, MA	18–57	Yes (3–7)	General
Unler and Murat [11]	LR	PSO, SS, TS	8–93	Yes (3–8)	General
García-Torres et al. [12]	NB	TS	9–70	No	General
El Ferchichi and Laabidi [13]	SVM	TS, GA	24	No	Urban transport
Al-Ani [14]	ANN	ACO	40, 50	No	Speech, image

Legends: Particle swarm optimization (PSO), genetic algorithm (GA), support vector machines (SVM), binary particle swarm optimization (BPSO), scatter search (SS), rough set (RS), ant colony optimization (ACO), tubu search (TS), discriminant analysis (DA), binary hierarchical classifier (BHC), memetic algorithm (ma), greedy randomized adaptive search (GRASP), logistic regression (LR) classifier: naive Bayes (NB), Classifier, and Artificial neural network (ANN).