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A Fast Classification Algorithm Using Concept Hierarchy Algorithm

D. Saranya, Dr. A. Bharathi

Abstract


Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. The algorithm used for classification is OneR, Naïve Bayes and C4.5 algorithm. This work use OneR, it is a simple classification algorithm that generates a one-level decision tree. OneR is able to infer typically simple, yet accurate, classification rules from a set of instances. This paper present Attribute Oriented Induction (AOI) has concept hierarchy as an advantage where concept hierarchy as a background knowledge which can be provided by knowledge engineers or domain experts. The experimental result shows that the proposed method of OneR with Attribute Oriented Induction program provides an accurate result by using UCI repository datasets.


Keywords


One Rule, Attribute Oriented Induction, Machine Learning Algorithm, Naive Bayes Algorithm

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References


Wu, Xindong, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. "Data mining with big data." Knowledge and Data Engineering, IEEE Transactions on 26, no. 1 (2014): 97-107.

Baker, R., and George Siemens. "Educational data mining and learning analytics." Cambridge Handbook of the Learning Sciences: (2014).

P. Giudici, “Applied Data Mining: Statistical Methods for Business and Industry”, New York: John Wiley, 2003.

C.L. Blake, C.J. Mertz, “UCI Machine Learning Databases”, http://mlearn.ics.uci.edu/databases/heartdisease/, 2004.

Altintas, Nihat, and Michael Trick. "A data mining approach to forecast behavior." Annals of Operations Research 216, no. 1 (2014): 3-22.

Sharan, Roneel V., and Tom J. Moir. "Comparison of multiclass SVM classification techniques in an audio surveillance application under mismatched conditions." In Digital Signal Processing (DSP), 2014 19th International Conference on, pp. 83-88. IEEE, 2014.

Shao, Yuan-Hai, Wei-Jie Chen, Jing-Jing Zhang, Zhen Wang, and Nai-Yang Deng. "An efficient weighted Lagrangian twin support vector machine for imbalanced data classification." Pattern Recognition 47, no. 9 (2014): 3158-3167.

H. Kaur, S.K. Wasan, “Empirical Study on Applications of Data Mining Techniques in Healthcare”, Journal of Computer Science 2(2), Pp. 194-200, 2006.

Y. Cai, N. Cercone, J. Han, “An attribute-oriented approach for learning classification rules from relational databases” IEEE Computer, 4, Pp. 295-304, 2002.

B. Thuraisingham, “A Primer for Understanding and Applying Data Mining”, IT Professional, Pp. 28-31, 2000.

Wei, Li Wei, Chuan Shen Wei, and Xia Qing Wan. "Data Classification Using Support Vector Machines with Mixture Kernels." Advanced Materials Research662 (2013): 936-939.

Ahmed, Abeer Badr El Din, and Ibrahim Sayed Elaraby. "Data Mining: A prediction for Student's Performance Using Classification Method." World Journal of Computer Application and Technology 2, no. 2 (2014): 43-47.

Seera, Manjeevan, and Chee Peng Lim. "A hybrid intelligent system for medical data classification." Expert Systems with Applications 41, no. 5 (2014): 2239-2249.

Shao, Yuan-Hai, Wei-Jie Chen, Jing-Jing Zhang, Zhen Wang, and Nai-Yang Deng. "An efficient weighted Lagrangian twin support vector machine for imbalanced data classification." Pattern Recognition 47, no. 9 (2014): 3158-3167.

J. Han, M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006.

M.K. Obenshain, “Application of Data Mining Techniques to Healthcare Data”, Infection Control and Hospital Epidemiology, 25(8), Pp. 690–695, 2004.


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