ASIC: Supervised multi-class classification using adaptive selection of information components

Zongxing Xie, Thiago Quirino, Mei Ling Shyu, Shu Ching Chen

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

1 Scopus citations

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 languageEnglish (US)
Title of host publicationICSC 2007 International Conference on Semantic Computing
Pages527-534
Number of pages8
DOIs
StatePublished - Dec 1 2007
EventICSC 2007 International Conference on Semantic Computing - Irvine CA, United States
Duration: Sep 17 2007Sep 19 2007

Publication series

NameICSC 2007 International Conference on Semantic Computing

Other

OtherICSC 2007 International Conference on Semantic Computing
CountryUnited States
CityIrvine CA
Period9/17/079/19/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Computer Science Applications

Fingerprint Dive into the research topics of 'ASIC: Supervised multi-class classification using adaptive selection of information components'. Together they form a unique fingerprint.

  • Cite this

    Xie, Z., Quirino, T., Shyu, M. L., & Chen, S. C. (2007). ASIC: Supervised multi-class classification using adaptive selection of information components. In ICSC 2007 International Conference on Semantic Computing (pp. 527-534). [4338390] (ICSC 2007 International Conference on Semantic Computing). https://doi.org/10.1109/ICSC.2007.52