Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis
Joint Authors
Segera, Davies
Mbuthia, Mwangi
Nyete, Abraham
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification.
Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters.
To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM).
The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel.
Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel.
In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel).
In comparison, two dual and two multiclass imbalanced standard microarray datasets were used.
Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.
American Psychological Association (APA)
Segera, Davies& Mbuthia, Mwangi& Nyete, Abraham. 2019. Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis. BioMed Research International،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1125124
Modern Language Association (MLA)
Segera, Davies…[et al.]. Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis. BioMed Research International No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1125124
American Medical Association (AMA)
Segera, Davies& Mbuthia, Mwangi& Nyete, Abraham. Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1125124
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125124