Effects of Pruning on Accuracy in Associative Classification

Mohammad Abrar, Alex Tze Hiang Sim

Abstract


A number of techniques are presented in the literature for pruning in both decision tree as well as rules based classifiers. The pruning is used for two purposes; namely, Improve performance, and improve accuracy. As the pruning is reducing the set of rules as well as the size of the tree, the probability of improvement in performance is, therefore high. While on the other side, the pruning may eliminate the interesting information which can lead to reducing the accuracy. In this research, the effects of pruning on the accuracy are studied in detail. The experiments were carried out on the same techniques with and without using pruning strategies and the results of both types are compared. The analysis of the five algorithms over fourteen datasets showed that the unwise selection of pruning strategy could reduce the accuracy.


Keywords


Rule pruning; Associative classification; Classification; Association rules mining; Data mining

Full Text:

PDF

References


W.H.B. Liu and Y. Ma, Integrating classification and association rule mining, in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 80 – 86, 1998.

W. Li, J. Han and J. Pei, Cmar: Accurate and efficient classification based on multiple class association rules, in Proceedings IEEE International Conference on Data Mining (ICDM 2001), pp. 369 – 376, 2001.

F. Thabtah, P. Cowling and Y. Peng, Mcar: multi-class classification based on association rule, in The 3rd ACS/IEEE International Conference on Computer Systems and Applications, p. 33, 2005.

J. Han, Cpar: Classification based on predictive association rules, in Proceedings of the Third SIAM International Conference on Data Mining, Vol. 3, pp. 331 – 335, Siam, 2003.

N. Abdelhamid, A. Ayesh, F. Thabtah, S. Ahmadi and W. Hadi, Mac: A multiclass associative classification algorithm, Journal of Information & Knowledge Management 11 (2), 2012.

T. Hastie, R. Tibshirani, J. Jerome and H. Friedman, The Elements of Statistical Learning, Vol. 1, Springer, New York, 2001.

G.W. Snedecor and W.G. Cochran, Statistical Methods, 8th edn., Ames, Iowa.

J.R. Quinlan, C4. 5: Programs for Machine Learning, Vol. 1, Morgan Kaufmann, 1993.

B. Liu, W. Hsu and Y. Ma, Integrating classification and association rule mining, in Proceedings of the 4th, pp. 80 – 86, 1998.

E. Baralis, S. Chiusano and P. Garza, On support thresholds in associative classification, in Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 553 – 558, ACM, 2004.

K. Bache and M. Lichman, UCI Machine Learning Repository, 2013.




DOI: http://dx.doi.org/10.26713%2Fjims.v9i4.1006

eISSN 0975-5748; pISSN 0974-875X