Binary whale optimization algorithm for dimensionality reduction

Autor: Xu Yu, Abdelazim G. Hussien, Diego Oliva, Angel A. Juan, Essam H. Houssein
Přispěvatelé: Fayoum University, Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3), Universidad de Guadalajara, Minia University, Qingdao University of Science and Technology
Rok vydání: 2020
Předmět:
0209 industrial biotechnology
Wilcoxon signed-rank test
Computer science
whale optimization algorithm (WOA)
General Mathematics
clasificacion
Binary number
Feature selection
reducció de dimensionalitat
Algorismes
02 engineering and technology
reducción de dimensionalidad
020901 industrial engineering & automation
feature selection
bWOA-S
0202 electrical engineering
electronic engineering
information engineering
Computer Science (miscellaneous)
Feature (machine learning)
bWOA-V
whale optimization algorithm
Engineering (miscellaneous)
dimensionality reduction
selecció de funcions
Algoritmos
WOA
business.industry
lcsh:Mathematics
Dimensionality reduction
Hyperbolic function
binary whale optimization algorithm
Pattern recognition
Classification
lcsh:QA1-939
classificació
classification
selección de características
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
Binary whale optimization algorithm
Algorithms
Curse of dimensionality
Zdroj: O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)
Mathematics, Vol 8, Iss 1821, p 1821 (2020)
Mathematics
Volume 8
Issue 10
Popis: Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon&rsquo
s rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time.
Databáze: OpenAIRE
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