The week's pick
Random Articles
Reseach Article
An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 159 - Number 9 |
| Year of Publication: 2017 |
| Authors: Neelima Dixit |
10.5120/ijca2017913066
|
Neelima Dixit . An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering. International Journal of Computer Applications. 159, 9 ( Feb 2017), 18-22. DOI=10.5120/ijca2017913066
Abstract
Here in this broadside a novel approach for the Discretization of Nonstop Characteristics for the Classification of various datasets is proposed. The Planned Procedure implemented here works in Two Phases, in the first stage K-means Clustering is applied on the dataset to cluster the data on the basis of classes available in the dataset and second is to classify the Clustered Data using Support Vector Machine Classifier. The various Untried results achieved on different datasets proves that the planned procedure provides less mean number of cuts and reduced mean discretization time and also provides higher accuracy with better Scalability.
References
- Z. Pawlak. Rough sets. International Journal of Computer and Information Sciences, 11(5):341–356, 1982.
- R.Jensen and Q.Shen. A Rough Set – Aided system for Sorting WWW Book-marks. In N.Zhong et al.(Eds.), Web Intelligence: Research and Development, 95-105, 2001.
- F. Min, L.J. Xie, Q.H. Liu, H.B. Cai, A divide-and-conquer discretization algorithm, in: Proc. of the 2nd Int. Conf. on Fuzzy Systems and Knowledge Discovery(FSKD 2005), LNAI, vol. 3613, Springer, 2005, p. 1277C1286.
- G.K. Singh, S. Minz, Discretization using clustering and rough set theory, in: Proc. of the 2007 Int. Conf. on Computing: Theory and Applications (ICCTA 2007), 2007, pp.330–336.
- P. Blajdo, J.W. Grzymala-Busse, Z.S. Hippe, M. Knap, T. Mroczek, L. Piatek, A comparison of six approaches to discretization — a rough set perspective, in: Proc. of the 3rd Int. Conf. on Rough Sets and Knowledge Technology (RSKT 2008), LNAI, vol. 5009, Springer, 2008, p. 31C38.
- D. Tian, X.J. Zeng, J. Keane, Core-generating discretization for rough set feature selection, in: Transactions on Rough Sets XIII, LNCS, vol. 6499, Springer, 2011, p. 135C158.
- H S Nguyen. Approximate Boolean Reasoning: Foundations and Applications in Data Mining, Transactions on Rough Sets, Lecture Notes in Computer Science, Springer, pages 334–506, 2006.
- Z Pawlak, A Skowron. Rough Sets and Boolean Reasoning, Information Sciences 177:41–73, 2007.
- Feng Jiang, Yuefei Sui, “A novel approach for discretization of continuous attributes in rough set theory”, 0950-7051, Elsevier, 2014.
- Grzymala-Busse, J.W. Discretization based on entropy and multiple scanning. Entropy 2013, 15, 1486–1502.
- Liu, H.; Hussain, F.; Tan, C.L.; Dash, M. Discretization: An enabling technique. Data Min. Knowl. Discov. 2002, 6, 393–423.
- Nandita Sengupta, Jaydeep Sen, Jaya Sil, Moumita Saha, “Designing of on line intrusion detection system using rough set theory and Q-learning algorithm” 0925-2312, Elsevier, 2013.
Index Terms
Keywords