Browse > Article
http://dx.doi.org/10.15207/JKCS.2017.8.2.027

A Classification Algorithm using Extended Representation  

Lee, Jong Chan (Dept. of Internet, Chungwoon University)
Publication Information
Journal of the Korea Convergence Society / v.8, no.2, 2017 , pp. 27-33 More about this Journal
Abstract
To efficiently provide cloud computing services to users over the Internet, IT resources must be configured in the data center based on virtualization and distributed computing technology. This paper focuses specifically on the problem that new training data can be added at any time in a wide range of fields, and new attributes can be added to training data at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set(with the newly added attributes). This paper proposes further development of the new inference engine that can handle the above case naturally. Rule generated from former data set can be combined with the new data set to form the refined rule.
Keywords
Classification; Rule Refinement; UChoo; Decision Tree; Attribute; Training Data; Convergence;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 P. N. Tan, M. SteinBach, V. Kumar,"Introduction to data mining", 2005
2 M. Kantardzic,"Data Mining : Concepts, Models, Methods, and Algorithms", Wiley-IEEE Press, 2002.
3 J.R.Quinlan, "C4.5 : Program for Machine Learning," San Mateo, Calif, Morgan Kaufmann, 1993.
4 P. E. Utgoff, "Incremental Induction of Decision Trees", Machine Learning, Vol. 4, No. 2, pp. 161- 186, 1989.   DOI
5 J.C.Lee, D.H.Seo, C.H.Song, W.D.Lee, "FLDF based Decision Tree using Extended Data Expression", The 6th Conference on Machine Learning & Cybernetics, Hong Kong, pp. 3478- 3483, Aug. 2007.
6 T. S. Lim, W. Y. Loh, Y. S. Shih,"A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Tree Old and New Classification Algorithms", Machine Learning, Vol. 40, No. 3, pp. 203-228, 2000.   DOI
7 R. Kohavi, J. R. Quinlan, "Data Mining Task and Methods: Classification: Decision-tree Discovery", Handbook of data mining and knowledge discovery press, pp. 267-276, 2002.
8 H. Schwenk, Y. Bengio,"Boosting neural networks" ,Neural Computation, Vol. 12, pp1869-1887, 2000.   DOI
9 R. Polikar, "Bootstrap-Inspired Techniques in Computational Intelligence", IEEE Signal Processing Magazine, pp. 59-72, 2007.
10 J. R. Quinlan,"Bagging, Boosting, and C4.5", AAAI/ IAAI, Vol. 1, 1996.
11 E. Keogh, C. Blake, C. J. Merz,"UCI Repository of Machine Learning Databases",http://www.ics.uci.edu/-mlearn/MLRepository.html, 1989.
12 K. Ryu, "Convergence Research for Implementing NC Postprocessor Based Cloud Computing", Journal of the Korea Convergence Society, Vol. 7, No. 1, pp. 17-23, 2016.   DOI
13 D. Kim, N Kim, "Design of Mixed Reality based Convergence Edutainment System using Cloud Service", Journal of the Korea Convergence Society, Vol. 6, No. 3, pp. 103-109, 2016   DOI
14 Y. Jung, J. Jeon, "A Fusion of the Period Characterized and Hierarchical Bayesian Techniques for Efficient Cluster Analysis of Time Series Data", Journal of Digital Convergence, Vol. 13, No. 7, pp. 169-175, 2015.   DOI
15 H. Lee, K. Park, D. Kim,"A Study on Possible Construction of Big Data Analysis System Applied to the Offline Market", Journal of Digital Convergence, Vol. 14, No. 9, pp. 317-323, 2016.   DOI
16 G. Kim, S. Jeong, H. Mun, C. Kim, "Design of Curve Road Detection System by Convergence of Sensor", Journal of Digital Convergence, Vol. 14, No. 8, pp253-259, 2016.   DOI