Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant fea...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:SN computer science Jg. 2; H. 4; S. 295
Hauptverfasser: Chantar, Hamouda, Tubishat, Mohammad, Essgaer, Mansour, Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.07.2021
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Coronaviruses
Data mining
Data Structures and Information Theory
Exploitation
Feature selection
Genetic algorithms
Heuristic
Information Systems and Communication Service
Machine learning
Optimization algorithms
Original Research
Pattern Recognition and Graphics
Performance evaluation
Simulated annealing
Simulation
Software Engineering/Programming and Operating Systems
Vision
Simulated annealing algorithm
Feature selection
Dragonfly algorithm
Optimization
ISSN:2662-995X, 2661-8907, 2661-8907
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2662-995X
2661-8907
2661-8907
DOI:10.1007/s42979-021-00687-5