Collateral representative subspace projection modeling for supervised classification

Thiago Quirino, Zongxing Xie, Mei-Ling Shyu, Shu Ching Chen, LiWu Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

In this paper, a novel supervised classification approach called Collateral Representative Subspace Projection Modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, Decision Table (DT), Nearest Neighbor (NN), KNN, Support Vector Machine (SVM), 1-NN Best Warping Window DTW, 1-NN DTW with no Warping Window, and the well-known classifier boosting method AdaBoost with SVM.

Original languageEnglish
Title of host publicationProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Pages98-105
Number of pages8
DOIs
StatePublished - Dec 1 2006
Event18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 - Arlington, VA, United States
Duration: Oct 13 2006Oct 15 2006

Other

Other18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
CountryUnited States
CityArlington, VA
Period10/13/0610/15/06

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Quirino, T., Xie, Z., Shyu, M-L., Chen, S. C., & Chang, L. (2006). Collateral representative subspace projection modeling for supervised classification. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI (pp. 98-105). [4031886] https://doi.org/10.1109/ICTAI.2006.42