Modified Mahalanobis Taguchi System for Imbalance Data Classification
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-24
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data.
Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification.
In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS).
To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms.
MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400.
A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA).
American Psychological Association (APA)
El-Banna, Mahmoud. 2017. Modified Mahalanobis Taguchi System for Imbalance Data Classification. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1141027
Modern Language Association (MLA)
El-Banna, Mahmoud. Modified Mahalanobis Taguchi System for Imbalance Data Classification. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1141027
American Medical Association (AMA)
El-Banna, Mahmoud. Modified Mahalanobis Taguchi System for Imbalance Data Classification. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1141027
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141027