IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 6,June 2012
Effects of Shannon entropy and J-Measurement for making rules by using decision list method
Full Text(PDF, )  PP.205-211  
Debaprasad Misra, Arindam Giri
Decision List, Entropy, KBS, Rule extraction, Rule evaluation, Shannon entropy, J-Measurement
Expert system makes lot of changes in our daily life. For making an expert system we need perfect, efficient and concise knowledge base system (KBS). The backbone of any KBS is the finest, optimum and exact 'rules' for any particular application that makes the success of the expert system. In this paper, we generate a few rules that come from a Soybean data set with different effect of Shannon entropy and J measurement by using as the rule evaluation parameter. The dissimilar out comes makes differentiate of the effect by using rule evaluation parameter. The experimental results are also focused the different error rate from Shannon entropy and J measurement and other effects that make the changes in the output. After that, we compare and evaluate the results and outputs from two cases, that represent in graph based layout and tabular representation. More over, a short review of KBS, entropy, decision list are also paying attention in this paper.
[1] Arash Ghorbannia Delavar,Mehdi Zekriyapanah Gashti,, Behroz nori Lohrasbi , Mohsen Nejadkheirallah,” RMSD: An optimal algorithm for distributed systems resource whit data mining mechanism”, Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition Vol. 2, No. 2, February 2011

[2] A.Ghorbannia Delavar , M.Zekriyapanah Gashti , B.Noori Lahrod ," ERPASD: A Novel Algorithm forIntegrated Distributed Reliable Systems Using Data Mining Mechanisms " , IEEE ICIFE 2010 , September 17-19, 2010, Chongqing, China

[3] Arash Ghorbannia Delavar , Narjes Rohani , Mehdi Zekriyapanah Gashti . “ERPAC: A Novel Framework for Integrated Distributed Systems Using Data Mining Mechanisms ", International Conference on Software Technology and Engineering. IEEE ICSTE 2010, October 3-5, 2010. San Juan, Puerto Rico, USA

[4] José C. Riquelme, Jesús S. Aguilar and Miguel Toro, “Discovering Hierarchical Decision Rules With Evoluive Algorithm in Supervised Learning” IJCSS, Vol.1, No.1, 2000

[5] Fariba Shadabi and Dharmendra Sharma, “Artificial Intelligence and Data Mining Techniques in Medicine – Success Stories”, 2008 International Conference on Bio Medical Engineering and Informatics

[6] Harleen Kaur and Siri Krishan Wasan, “Empirical Study on Applications of Data Mining Techniques in Healthcare “

[7] Data Mining: Concepts and Techniques Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001

[8] Portia A. Cerny “Data mining and Neural Networks from a Commercial Perspective “

[9] Antony Browne, Brian D. Hudson; , David C. Whitley , Martyn G, Philip Picton “Biological data mining with neural networks: implementation and application of a flexile decision tree extraction algorithm to genomic problem domains”

[10] K. Mumtaz1 S. A. Sheriff 2 and K. Duraiswamy , “Evaluation of Three Neural Network Models using Wisconsin Breast Cancer Database” Journal of Theoretical and Applied Information Technology P 37 -42

[11] A.Vesely “NEURAL NETWORKS IN DATA MINING” AGRIC. ECON. – CZECH, 49, 2003 (9): 427–431

Untitled Page