Browse > Article
http://dx.doi.org/10.15207/JKCS.2018.9.12.009

Application Examples Applying Extended Data Expression Technique to Classification Problems  

Lee, Jong Chan (Deptartment of Computer Engineering, ChungWoon University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.12, 2018 , pp. 9-15 More about this Journal
Abstract
The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.
Keywords
Extended data expression; Classification; Learning; Rule refinement; Missing data;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 J. C. Lee, & W.D.Lee.(2012) Biological early warning system using UChoo algorithm, Journal of Information and Communication Convergence Engineering, 16(1)
2 J.Wu, Y.S.Kim, C.H.Song, & W.D.Lee.(2008) A new classifier to deal with incomplete data, International Conference on Software Engineering, Artificial Intelligence, Networking , 105-110
3 K.Yang, A.Kolesnikova, & W.D.Lee.(2013) A new incremental learning algorithm with probabilistic weight using extended data expression, Journal of Information and Communication Convergence Engineering, 11(4), 258-267   DOI
4 Y. L. Cun, Y. Bengio, & G. Hinton.(2015) Deep learning. Nature, 521(7553), 436-444. DOI : 10.1038/nature14539   DOI
5 J. Lee. (2018) A method of eye and lip region dectection using faster R-CNN in face image, Journal of the Korea Convergence Society, 9(1), 1-8, https://doi.org/10.15207/JKCS.2018.9.8.001   DOI
6 J. Z. Kolter & M. A. Maloof(2003), Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift, Proceedings of the Third International IEEE Conference on Data Mining, 123-130.
7 J. Z. Kolter, & M. A. Maloof. (2007). Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts, Journal of Machine Learning Research 8 (2007) 2755-2790.
8 J.R.Quinlan. (1993) C4.5 : Program for Machine Learning, San Mateo, Calif, Morgan Kaufmann
9 D. Kim, D. Lee, & W. D. Lee. (2006) Classifier using extended data expression, IEEE Mountain Workshop on Adaptive and Learning Systems, 154-159
10 D. Kim, D. Seo, Y. Li, & W. D. Lee.(2008) A classifier capable of rule refinement, International Conference on Service Operations and Logistics, and Informatics, 168-173.
11 J. M. Kong, D. H. Seo, & W. D. Lee.(2007) Rule refinement with extended data expression, Sixth International Conference on Machine Learning and Applications, 310-315
12 D.H.Lee, C.Song, & W.D.Lee.(2007), A classifier capable of handling new attributes, IEEE Symposium on Computational Intelligence and Data Mining, 323-327.
13 J. W. Friedman. (1977), A recursive partitioning decision rule for non parametric classification, IEEE Transaction on Computer Science, 404-408.
14 R. J. Hathaway, & J. C. Bezdek. (2001) Fuzzy c-means clustering of incomplete data, IEEE Transaction on systems, Man and Cybernetics -part B: Cybernetics, 31(5).
15 J. Han, & M.Damber.(2001) Data mining : concept and techniques, Morgan Kaufmann Publishers
16 T.P.Hong, L.H.Tseng, & B.C.Chien.(2002) Learning fuzzy rules from incomplete numerical data by rough sets, IEEE international conference on Fuzzy Systems, 1438-1443