A Novel Approach for Classification on Breast Cancer Data Set

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Rough set theory is an intelligent technique used for the discovery of data reduction, approximate set classification, and rule induction from databases. In this paper, we present a rough set method for generating classification rules from a set of observed 699 samples of the breast cancer data. The rough set reduction technique based on discernibility matrix with some modification(K- Map) is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Here we are use LEM2 algorithm to generate rules.Experimental results from applying the rough set analysis to the set of data samples are given and evaluated.

[1]  Julie M. David,et al.  Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children , 2010 .

[2]  Yuichi Kato,et al.  Studies on an effective algorithm to reduce the decision matrix — A technique on a rule extraction by rough sets theory , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[3]  Da Ruan,et al.  A parallel method for computing rough set approximations , 2012, Inf. Sci..

[4]  Yuichi Kato,et al.  Studies on an Effective Algorithm to Reduce the Decision Matrix , 2011, RSFDGrC.

[5]  Asit Kumar Das,et al.  Single Reduct Generation Based on Relative Indiscernibility of Rough Set Theory , 2012, ArXiv.

[6]  Miguel A. Carreira-Perpinan,et al.  Dimensionality Reduction , 2011 .

[7]  Aboul Ella Hassanien,et al.  Rough Set Approach for Generation of Classification Rules of Breast Cancer Data , 2004, Informatica.

[8]  Puniethaa Prabhu,et al.  Feature selection for HIV database using rough system , 2010, 2010 Second International conference on Computing, Communication and Networking Technologies.

[9]  D. Bhattacharyya,et al.  Efficient Rule Set Generation using Rough Set Theory for Classification of High Dimensional Data , 2011 .

[10]  Thomas Bräunl,et al.  A framework of adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS) , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[11]  W. Campbell,et al.  THE UNIVERSITY OF TEXAS AT DALLAS , 2004 .

[12]  Prerna Mahajan,et al.  Rough Set Approach in Machine Learning: A Review , 2012 .

[13]  Amitava Chatterjee,et al.  Influential rule search scheme (IRSS) - a new fuzzy pattern classifier , 2004, IEEE Transactions on Knowledge and Data Engineering.