An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis
Joint Authors
EL bendadi, Khawla
Lakhdar, Yissam
Sbai, El Hassan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Within the kernel methods, an improved kernel credal classification algorithm (KCCR) has been proposed.
The KCCR algorithm uses the Euclidean distance in the kernel function.
In this article, we propose to replace the Euclidean distance in the kernel with a regularized Mahalanobis metric.
The Mahalanobis distance takes into account the dispersion of the data and the correlation between the variables.
It differs from Euclidean distance in that it considers the variance and correlation of the dataset.
The robustness of the method is tested using synthetic data and a benchmark database.
Finally, a set of DNA microarray data from Leukemia dataset was used to show the performance of our method on real-world application.
American Psychological Association (APA)
EL bendadi, Khawla& Lakhdar, Yissam& Sbai, El Hassan. 2018. An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130833
Modern Language Association (MLA)
EL bendadi, Khawla…[et al.]. An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130833
American Medical Association (AMA)
EL bendadi, Khawla& Lakhdar, Yissam& Sbai, El Hassan. An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130833
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130833