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General Information
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.

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