Mahalanobis-Taguchi System for Symbolic Interval Data Based on Kernel Mahalanobis Distance
Mahalanobis-Taguchi System (MTS), as a pattern recognition method by constructing a continuous measurement scale, has a very good performance on classification and feature selection for real-valued data. However, the record of symbolic interval data has become a common practice with the recent advan...
Main Authors: | Zhipeng Chang, Wenhe Chen, Yuping Gu, Haoyue Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8962011/ |
Similar Items
-
Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System
by: Seul-Gi Kim, et al.
Published: (2021-08-01) -
Anomaly Detection in a Logistic Operating System Using the Mahalanobis–Taguchi Method
by: Takumi Asakura, et al.
Published: (2020-06-01) -
Classification Performance of Thresholding Methods in the Mahalanobis–Taguchi System
by: Faizir Ramlie, et al.
Published: (2021-04-01) -
On the Influence of Reference Mahalanobis Distance Space for Quality Classification of Complex Metal Parts Using Vibrations
by: Liangliang Cheng, et al.
Published: (2020-12-01) -
Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic
by: Jayro Santiago-Paz, et al.
Published: (2015-09-01)