An Experimental Evaluation of Case Slicing as a New Classification Technique

Authors

  • Omar A. A. Shiba Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Md. Nasir Sulaiman Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Fatimah Ahmad Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Ali Mamat Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.32890/jict2003.2.2.6

Keywords:

Data mining, case-slicing technique, k-nearest neighbour, base learning algorithm, naïve bayes, classification accuracy

Abstract

Several classification techniques are designed to discover such classifications when the classifications are unknown. The techniques are tested and evaluated, however, by matching the classifications they recover against expected classifications. Several such techniques may be compared by experimentally evaluating their performance on the same datasets. The goal of this paper is to evaluate the case slicing technique as a new classification technique. The paper achieves this goal in three steps: Firstly, it introduces the case slicing technique as a new approach. Secondly, the paper presents applications of this technique on several datasets. Lastly, it compares the proposed approach with other selected approaches such as the K-Nearest Neighbour (K-NN), Base Learning Algorithm (C4.5) and Naïve Bayes classifier (NB) in solving the classification problems. The results obtained shows that the proposed approach is a promising method in solving decision-making problem.

 

References

Aggrawal, C.C., & Philip, S.Y. (1998). Data mining techniques for associations, clustering and classification. In IBM Lecture Notes in Computer Science. T. J. Watson Research Center, Yorktown Heights: Springer-Verlag Heidelberg. t.uum.edu.my/ //ic http Journal of ICT, 2 (2), pp: 105-

Aha, D.W. (1998). Feature weighting for lazy learning algorithms. (Technical Repott AIC-98-003). Washington, DC: Naval Research, Laboratory, Navy Center for Applied Research in Artificial Intelligence.

Biberman, Y. (1994). A context similarity measure. In Proceedings of the European Conference on Machine Learning (ECML-94). (pp. 49-63) Catalina, Italy: Springer Verlag.

Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. In Proceedings of the European Working Session on Learning, (pp. 164-178) Porto, Portugal.

Ching, J.Y., Wong, AKC. & Chan, KCC. (1995). Class-dependent discretization for inductive learning from continuous and mixed mode data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(7), 041-651.

Cost, S., & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features.. Machine Learning, 10, 57-78.

Domingos, P. (1995). Rule induction and instance-based learning: A unified approach. In Proceeding of the International Joint Conference on Artificial Intelligence (IJCAI-95), (pp. 1226-1232) Montreal.

Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous feature. In Proc. of the 12" International Conference on Machine Learning, (pp. 194-202) Melbourne, Australia.

Edwin, D. (1974). Recent progress in distance and similarity measures in pattern recognition. Second International Joint Conference on Pattern Recognition, (pp. 534-539) Copenhagen, Denmark.

Fayyad, U.M., & Irani, K.B. (1992). On the handling of continuous-valued attributes in decision three generation. Machine Learning, 8, 87-102.

Kamkar, M., (1995). An overview and comparative classification of program slicing techniques. Journal of System Software, 31, 197-214.

Ling, X.C., & Zhang, H. (2002). Toward bayesian classifiers with accurate probabilities. In Proceedings of the Sixth Pacifie-Asia Conference on KDD, (pp. 123-134), Taipei, Taiwan : Springer. t.uum.edu.my/ //ic http Journal of ICT, 2 (2), pp: 105-

Michalski, R.S., Robert, E.S., & Edwin, D. (1981). A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. In Laveen N. Kanal and Azriel Rosenfeld (Eds.). Progress in pattern recognition, 1,33-56, New York: North-Holland.

Mitchell, T. M. (1997). Machine learning. New York:McGraw-Hill,.

Mohri, T., & Tanaka, H. (1994). An optimal-weighting criterion of case indexing for both numeric and symbolic attributes. In D.W. Aha (Ed.), Case-based reasoning, (Technical Report WS-94-01) (pp. 123-127). Menlo Park, CA: AIII Press.

Murphy, P.M. (1996). UCT repositories of machine learning and domain theories. Retrieved November 12, 2002, from http://www.isc.uciedu/~mlearn/ MLRepository.html

Nadler, M., & Eric, P. S. (1993). Pattern recognition engineering. (pp. 293-294). New York: Wiley.

Pfahringer, B. (1995). Compression-based discretization of continuous attributes. In Proc. of the 12% International Conference on Machine Learning, (pp. 456-463). Melbourne, Australia.

Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1(1), 81—106.

Quinlan, J.R. (1993). C45: Programs for Machine Learning, CA: Morgan Kaufmann Publishers, Inc.

Rachlin, J., Simon, K., Salzberg, S., & David, W.A. (1994). Towards a better understanding of memory-based and Bayesian classifiers. In Proceedings of the Eleventh International Machine Learning Conference, (pp. 242-250). New Brunswick, NJ: Morgan Kaufmann.

Randall, DW. & Tony, R.M. (1997). Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1-3.

Salzberg, S. (1991). A nearest hyperrectangle learning method. Machine Learning, 6, 277-309. t.uum.edu.my/ //ic http Journal of ICT, 2 (2), pp: 105-iii

Tip, F. (1995). A survey of program slicing techniques. Journal of Programming Languages, 3, 121-189.

Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327-352.

Vasconcelos, W.W. (2000). Slicing knowledge-based systems techniques and applications. Knowledge Based Systems Journal, 13, 177-198.

Weiser, M. (1984). Program slicing. IEEE Transaction Software Engineering, SE-10 (4), 352-357.

Wettschereck, D., & Aha, D.W. (1995). Weighting Features. In Proceedings of the 1 International Conference on CBR (ICCBR-95). (pp. 347-358). Portugal.

Wilson, D.R., & Martinez, T.R. (1996). Value difference metrics for continuously valued attributes. In Proceedings of the International Conference on Antificial Intelligence, Expert Systems and Neural Networks, (pp. 11-14).

Xiaoli, Q. (1999). A case-based reasoning system for bearing design. (Mastet’s Thesis), Faculty of Computer Science, Drexel University. Philadelphia, PA

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Published

26-11-2003

Issue

Vol. 2 No. 2 (2003): Journal of Information and Communication Technology (JICT) Vol. 2, No.2 Dec 2003

Section

Articles

How to Cite

A. Shiba, O. A., Sulaiman, M. N., Ahmad, F., & Mamat, A. (2003). An Experimental Evaluation of Case Slicing as a New Classification Technique. Journal of Information and Communication Technology, 2(2), 105-117. https://doi.org/10.32890/jict2003.2.2.6

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Identifiers DOI 10.32890/jict2003.2.2.6 OpenAlex W2582514020