Abstract
In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce further computational load requirements, and (iii) conduct supervised classification with the C-RSPM (Collateral Representative Subspace Projection Modeling) approach. Experimental results on a variety of data sets have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
| Original language | English |
|---|---|
| Title of host publication | ICSC 2007 International Conference on Semantic Computing |
| Pages | 527-534 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2007 |
| Event | ICSC 2007 International Conference on Semantic Computing - Irvine CA, United States Duration: Sep 17 2007 → Sep 19 2007 |
Other
| Other | ICSC 2007 International Conference on Semantic Computing |
|---|---|
| Country | United States |
| City | Irvine CA |
| Period | 9/17/07 → 9/19/07 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
Cite this
ASIC : Supervised multi-class classification using adaptive selection of information components. / Xie, Zongxing; Quirino, Thiago; Shyu, Mei-Ling; Chen, Shu Ching.
ICSC 2007 International Conference on Semantic Computing. 2007. p. 527-534 4338390.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - ASIC
T2 - Supervised multi-class classification using adaptive selection of information components
AU - Xie, Zongxing
AU - Quirino, Thiago
AU - Shyu, Mei-Ling
AU - Chen, Shu Ching
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce further computational load requirements, and (iii) conduct supervised classification with the C-RSPM (Collateral Representative Subspace Projection Modeling) approach. Experimental results on a variety of data sets have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
AB - In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce further computational load requirements, and (iii) conduct supervised classification with the C-RSPM (Collateral Representative Subspace Projection Modeling) approach. Experimental results on a variety of data sets have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
UR - http://www.scopus.com/inward/record.url?scp=47749107229&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47749107229&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2007.52
DO - 10.1109/ICSC.2007.52
M3 - Conference contribution
SN - 0769529976
SN - 9780769529974
SP - 527
EP - 534
BT - ICSC 2007 International Conference on Semantic Computing
ER -