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 language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI |
| Pages | 98-105 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2006 |
| Event | 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 - Arlington, VA, United States Duration: Oct 13 2006 → Oct 15 2006 |
Other
| Other | 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 |
|---|---|
| Country | United States |
| City | Arlington, VA |
| Period | 10/13/06 → 10/15/06 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Collateral representative subspace projection modeling for supervised classification. / Quirino, Thiago; Xie, Zongxing; Shyu, Mei-Ling; Chen, Shu Ching; Chang, LiWu.
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. 2006. p. 98-105 4031886.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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, LiWu
PY - 2006/12/1
Y1 - 2006/12/1
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
UR - http://www.scopus.com/inward/citedby.url?scp=38349093568&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2006.42
DO - 10.1109/ICTAI.2006.42
M3 - Conference contribution
SN - 0769527280
SN - 9780769527284
SP - 98
EP - 105
BT - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ER -