Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhanced spectral embedding with semi-supervised feature selection
ICNC'09 Proceedings of the 5th international conference on Natural computation
A high-order feature synthesis and selection algorithm applied to insurance risk modelling
International Journal of Business Intelligence and Data Mining
Applying the Mahalanobis-Taguchi strategy for software defect diagnosis
Automated Software Engineering
Information Technology and Management
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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.