Multiclass MTS for Simultaneous Feature Selection and Classification

• Published in: IEEE transactions on knowledge and data engineering 2009-02, Vol.21 (2), p.192-205
• Main Authors: SU, Chao-Ton, HSIAO, Yu-Hsiang
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Chaos; Classification; Construction; Data mining; Diabetes; Diabetes mellitus; Exact sciences and technology; Experimental design; Extraterrestrial measurements; feature selection; Gain; gestational diabetes mellitus; Gram-Schmidt orthogonalization process; Gynecology. Andrology. Obstetrics; Image recognition; Machine learning; Mahalanobis-Taguchi system (MTS); Mathematical models; Mathematics; Medical sciences; multiclass problem; Orthogonal arrays; Probability and statistics; Robustness; Sciences and techniques of general use; Signal to noise ratio; Speech recognition; Statistics; Studies; Support vector machine classification; Support vector machines; weighted Mahalanobis distance
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2008.128

Multiclass Mahalanobis-Taguchi system (MMTS), the extension of MTS, is developed for simultaneous multiclass classification and feature selection. In MMTS, the multiclass measurement scale is constructed by establishing an individual Mahalanobis space for each class. To increase the validity of the measurement scale, the Gram-Schmidt process is performed to mutually orthogonalize the features and eliminate the multicollinearity. The important features are identified using the orthogonal arrays and the signal-to-noise ratio, and are then used to construct a reduced model measurement scale. The contribution of each important feature to classification is also derived according to the effect gain to develop a weighted Mahalanobis distance which is finally used as the distance metric for the classification of MMTS. Using the reduced model measurement scale, an unknown example will be classified into the class with minimum weighted Mahalanobis distance considering only the important features. For evaluating the effectiveness of MMTS, a numerical experiment is implemented, and the results show that MMTS outperforms other well-known algorithms not only on classification accuracy but also on feature selection efficiency. Finally, a real case about gestational diabetes mellitus is studied, and the results indicate the practicality of MMTS in real-world applications.