NDLI: ASIC: Supervised Multi-class Classification using Adaptive Selection of Information Components

Content Provider	IEEE Xplore Digital Library
Author	Zongxing Xie Quirino, T. Mei-Ling Shyu Shu-Ching Chen
Copyright Year	2007
Description	Author affiliation: Univ. of Miami, Coral Gables (Zongxing Xie; Quirino, T.; Mei-Ling Shyu)
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.
Starting Page	527
Ending Page	534
File Size	358516
Page Count	8
File Format	PDF
ISBN	9780769529974
DOI	10.1109/ICSC.2007.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Application specific integrated circuits Support vector machines Radio frequency Image retrieval Support vector machine classification Training data Data mining Distributed computing Principal component analysis Logistics
Content Type	Text
Resource Type	Article

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