TY - GEN
T1 - Collateral representative subspace projection modeling for supervised classification
AU - Quirino, Thiago
AU - Xie, Zongxing
AU - Shyu, Mei Ling
AU - Chen, Shu Ching
AU - Chang, Li Wu
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38349093568&partnerID=8YFLogxK
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U2 - 10.1109/ICTAI.2006.42
DO - 10.1109/ICTAI.2006.42
M3 - Conference contribution
AN - SCOPUS:38349093568
SN - 0769527280
SN - 9780769527284
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 98
EP - 105
BT - Procedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
T2 - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
Y2 - 13 October 2006 through 15 October 2006
ER -