Applying CST on Medical Datasets
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Classification Accuracy, Case Slicing Technique, Decision Making, Slicing
Abstract
An important component of many data mining projects is finding a good classification algorithm; the Case Slicing Technique (CST) is a classification algorithm based on program slicing techniques that is examined in solving the classification problems in the medical domain. The technique is experimented with three medical datasets: Hepatitis Domain (HEPA), Heart Disease (CLEV), and Breast Cancer (BCO) datasets. The experimental results are compared with other classification algorithms, K-Nearest Neighbor (K-NN) and Naïve Bayes (NB). The experimental result shows that the slicing technique is a promising classification algorithm in solving the decision-making in the medical classification problem.
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References
[1]-Weiser, M.(1984), Program Slicing, IEEE Trans. Software Eng., SE-10(4), 352-357.
[2]-Agrawal, H.; and Horgan, J.R.,(1990).Dynamic Program Slicing, in Proceeding of the ACM SIGPLAN’90 Conference on Programming Language Design and implementation, New York, 246-256.
[3]-Frank Tip(1995).A Survey of Program Slicing Techniques, Journalof Programming Languages;3: 121-189.
[4]-Wemberto W. Vasconcelos(2000), Slicing Knowledge-Based Systems Techniques and Applications, Knowledge basedSystems Journal., Elsevier; 13: 177-198.
[5]-Kamkar M. (1995). An Overview and Comparative Classification of Program Slicing Techniques”, J. System Software; 31:197-214.
[6]-Ming Dong, Ravi Kothari(2003). Feature Subset Selection Using a New Definition of Classifiability Computer Science Department, Wayne State University.
[7]-Omar A. Shiba, Md. Nasir Sulaiman, Ali Mamatand Fatimah Ahmad, (2006). An Efficient and Effective Case Classification Method Based On Slicing, International Journal of The Computer, the Internet and Management Vol. 14.No.2, 15-23.
[8]-O. Shiba (2010). Towards a Better Feature Subset Selection Approach-in proceedings of Knowledge Management 5thInternational Conference (KMICe 2010) 675-678.
[9]-Thair N. Phyu (2009). Survey of Classification Techniques in Data Mining. In Proceeding of the International MultiConference of Engineering and Computer Scientists Vol I.
[10]-Xiaoli, Q. (1999).A Case-Based Reasoning System for Bearing Design, Master Thesis Submitted to the Faculty of Drexel University.
[11]-Tzung-I Tang, at. el. (2005). A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis. IEMS Vol. 4, No. 1, pp. 102-108.
[12]-Necip Fazil Ayan (1999).Using Information Gain as Feature Weight, 8thTurkish Symposium on Artificial Intelligence and Neural Networks, (TAINN’99).
[13]-Murphy, P. M.(1996).UCI Repositories of Machine Learning and Domain Theories [online]. University of California, Irvine Available: http://www.isc.uci.edu/~mlearn/MLRepository.html, [2017, Apr. 12 -Date of brows].
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