Abstract
The integration of supervised classification and association rules for building classification models is not new. One major advantage is that models are human readable and can be edited. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Pruning unnecessary rules without jeopardizing the classification accuracy is paramount but very challenging. In this paper we study strategies for classification rule pruning in the case of associative classifiers.
Partially supported by Alberta Ingenuity Fund, iCORE and NSERC Canada
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. Intl. Conf. on Very Large Data Bases, pp. 487–499 (1994)
Antonie, M.-L., Zaïane, O.R.: Text document categorization by term association. In: Proc. of the IEEE International Conference on Data Mining (ICDM 2002), pp. 19–26 (2002)
Antonie, M.-L., Zaïane, O.R., Coman, A.: Associative classifiers for medical images. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) MDM/KDD 2002 and KDMCD 2002. LNCS (LNAI), vol. 2797, pp. 68–83. Springer, Heidelberg (2003)
Bayardo, R.: Brute-force mining of high-confidence classification rules. In: 3rd Intl. Conf. on Knowledge Discovery and Data Mining (KDD 1997), pp. 123–126 (1997)
Coenen, F., Leng, P.: An evaluation of approaches to classification rule selection. In: IEEE International Conference on Data Mining (ICDM 2004), pp. 359–362 (2004)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: IEEE International Conference on Data Mining (ICDM 2001), pp. 369–376 (2001)
Liu, B., Hsu, H., Ma, Y.: Integrating classification and association rule mining. In: 4th Intl. Conf. on Knowledge Discovery and Data Mining (KDD 1998), pp. 80–86 (1998)
UCI repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Zaïane, O.R., Antonie, M.-L.: Classifying text documents by associating terms with text categories. In: Proc. of the Thirteenth Australasian Database Conference (ADC 2002), pp. 215–222 (2002)
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Zaïane, O.R., Antonie, ML. (2005). On Pruning and Tuning Rules for Associative Classifiers. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_136
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DOI: https://doi.org/10.1007/11553939_136
Publisher Name: Springer, Berlin, Heidelberg
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