Classification Rule Discovery Using Genetic Algorithm-Based Approach
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - August Issue 2013 by IJCTT Journal|
|Volume-4 Issue-8 |
|Year of Publication : 2013|
|Authors :Syed Shaheena, Shaik Habeeb|
Syed Shaheena, Shaik Habeeb"Classification Rule Discovery Using Genetic Algorithm-Based Approach"International Journal of Computer Trends and Technology (IJCTT),V4(8):2710-2715 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- — Data mining has a goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, which make it infeasible to search exhaustively. As a search strategy Genetic Algorithms was introduced by J.H. Holland have been applied successfully in many fields. Data Mining is acknowledged as an effective technique for the problem ‘abundant data but poor knowledge’. As the kernel of DM technique, the mining algorithms are investigated extensively; it will generate the exact class description for the classification of unknown data by analyzing the existing data. A genetic algorithm generates formulas for extracting the high-level classification/prediction rules with the following form. IF some conditions are satisfied THEN predict the value of some goal attribute. Genetic algorithms cannot deal with the data directly the data has to be encoded in the form of a chromosome. Based on the notion of survival of the fittest, a new population is formed to consist of the fittest rules in the current population, as well as offspring of these values. Offspring are created by applying genetic operators such as crossover and mutation. In crossover substrings from pair of rules are swapped to form the new rules, in mutation randomly selected bits in a rule strings are inverted. In this work we are implementing the rule discovery for Indian Liver Patient database (ILPD) collected from the north east of Andhra Pradesh, India.
 Zhu, X. and Davidson, I. 2007. Knowledge Discovery and Data Mining Challenges and Realities. IGI Global.
 Fayyad, U. M., Piatetsky-Sharpio, G. and Smyth, P.1996. From mining to knowledge discovery : An overview. In: Fayyad, U .M., Piatetsky-Sharpio, G. Smyth. P. and Uthurusany, R. (eds.)Advances in knowledge discovery and data mining , AAAI/MIT Press, pp. 1-34.
 Han, J., Kamber, M. and Pei, J. 2011. Data Mining: Concepts and Techniques. Third Edition, Morgan Kaufmann.
 Freitas, A. A. 2002. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg.
 Yogita, Saroj and Kumar, D. 2009. Rule +Exceptions: Automated discovery of comprehensible decision Rules. IEEE International Advance Computing Conference (IACC2009), Patiala, India, pp. 1479-1483.
 Barros, R.C., Basgalupp, M.P., Ferreira, A.C. and Frietas, A.A. 2011. Towards the automatic design of decision tree induction algorithms. In: GECCO (Companion Material ), Dublin, Ireland, pp. 567-574.
 Bramer, M. 2007. Principles of Data Mining. Springer-Verlag London Limited.
 Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
 Dehuri, S. and Mall, R. 2006. Predictive and comprehensible rule discovery using a multi objective genetic algorithms. Knowledge Based Systems, vol. 19, pp. 413-421.
 Fidelis, M.V., Lopes, H.S., Freitas, A.A. and Grossa, P. 2000. Discovering comprehensible classification rules with a genetic algorithm.
Keywords : Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning, Genetic Algorithm, Classification Rule, Genetic Operators, Fitness Function, Predictive Accuracy.