• Apr 08, 2019 News! [CFP] 2019 the annual meeting of IJET Editorial Board, ICEDA 2019, will be held in Bali, Indonesia during October 19-21, 2019.   [Click]
  • May 15, 2019 News! Vol.9, No.5- Vol.10, No.5 has been indexed by EI(Inspec)!   [Click]
  • Aug 28, 2019 News!Vol.11, No. 5 has been published with online version.   [Click]
General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. eng. technol.(Online)
    • Frequency:  Bimonthly
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusEI (INSPEC, IET).
    • E-mail: ijet@vip.163.com
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2012 Vol.4(1): 1-6 ISSN: 1793-8236
DOI: 10.7763/IJET.2012.V4.309

LACI: Lazy Associative Classification using Information Gain

S. P. Syed Ibrahim, K. R. Chandran, and C. J. Kabila Kanthasamy

Abstract—Associative classification method applies association rule mining technique in classification and achieves higher classification accuracy. However, it is a known fact that associative classification typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence, generating, ranking and selecting a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but indeed a challenging task. Lazy learning associative classification method eliminates the need of constructing the classifier but suffers with high computation cost. This paper proposes lazy associative classification using Information gain where, the system first chooses the Information gained attribute from the training sample and computes highest subset probability and then it directly predicts the class label. This proposed method not only reduces the computation cost but also improves the classification accuracy. Experimental result shows that the proposed system outperforms the traditional associative classification methods and the existing lazy associative classification method.

Index Terms—Associative classification, information gain, lazy learning.

S. P. Syed Ibrahim, K. R. Chandran and C. J. Kabila Kanthasamy are with Department of Computer Science and Engineering, PSG College of Technology, Coimbatore-641004, Tamilnadu, India (e-mail: sps_phd@yahoo.co.in; chandran_k_r@yahoo.co.in; cjkapila@gmail.com).


Cite: S. P. Syed Ibrahim, K. R. Chandran, and C. J. Kabila Kanthasamy, "LACI: Lazy Associative Classification using Information Gain," International Journal of Engineering and Technology vol. 4, no. 1, pp. 1-6, 2012.

Copyright © 2008-2019. International Journal of Engineering and Technology. All rights reserved. 
E-mail: ijet@vip.163.com