Swarm Intelligence Algorithms for Feature Selection: A Review

Abstract

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

“…In recent years, inspired by natural phenomena, a variety of novel meta-heuristic algorithms have been reported, e.g., bat algorithm (BA) [23], amoeboid organism algorithm [24], animal migration optimization (AMO) [25], artificial plant optimization algorithm (APOA) [26], biogeography-based optimization (BBO) [27,28], human learning optimization (HLO) [29], krill herd (KH) [30][31][32], monarch butterfly optimization (MBO) [33], elephant herding optimization (EHO) [34], invasive weed optimization (IWO) algorithm [35], earthworm optimization algorithm (EWA) [36], squirrel search algorithm (SSA) [37], butterfly optimization algorithm (BOA) [38], salp swarm algorithm (SSA) [39], whale optimization algorithm (WOA) [40], and others. A review of swarm intelligence algorithms can be referred to [41].…”

Diabetic Foot Risk Classification using Decision Tree and Bio-Inspired Evolutionary Algorithms

“…Automation of feature selection was done using different evolutionary algorithms shown in [13,14] such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), FireFly (FF), Dragon Fly (DF) and Gravitational Search Algorithm (GSA). Since every algorithm gives a different set of features in each run, the features are selected by running each algorithm for seven times and the features with mode>3 are selected under each algorithm.…”

Primerjava osnovnega algoritma po vzoru obnašanja netopirjev in njegove hibridne različice HBA

“…Algoritmi po vzoru iz narave posnemajo delovanje različnih naravnih in bioloških sistemov [17,3]. Večinoma se uporabljajo za reševanje kompleksnih optimizacijskih problemov [3], kot so na primer sestava urnikov, izbira najboljše lokacije za postavitev antene in iskanje najkrajše poti na grafu. Med tovrstne algoritme uvrščamo tudi algoritme inteligence rojev [9], ki so dandanes prisotni vštevilnih realnih aplikacijah [8].…”