Assessing Rough Classifiers

Since its introduction a prime area of application of rough sets theory has been in the field of classification. In this area rough sets theory provides a powerful toolbox of methods to deal with incomplete and contradicting information. Obviously, the assessment of the obtained classification results is of crucial importance. In our paper, we propose and evaluate some rough performance indices to evaluated the quality of bi-and multinomial classifiers. To illustrate their characteristics we perform comparative experiments on a synthetically generated data set.

[1]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[2]  Yang Xu.  Traffic incident detection based on HMM , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

[3]  Jyoti.  Rough Set Theory and Its Applications , 2013 .

[4]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5]  Yiyu Yao,et al.  Probabilistic rough set approximations , 2008, Int. J. Approx. Reason..

[6]  Yuming Zhou,et al.  An improved accuracy measure for rough sets , 2005, J. Comput. Syst. Sci..

[7]  Ian Witten,et al.  Data Mining , 2000 .

[8]  Yiyu Yao,et al.  Probabilistic approaches to rough sets , 2003, Expert Syst. J. Knowl. Eng..

[9]  Jerzy W. Grzymala-Busse,et al.  Generalized Parameterized Approximations , 2011, RSKT.

[10]  Sushmita Mitra.  An evolutionary rough partitive clustering , 2004, Pattern Recognit. Lett..

[11]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[12]  Dominik Slezak,et al.  The investigation of the Bayesian rough set model , 2005, Int. J. Approx. Reason..

[13]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[14]  Wojciech Ziarko,et al.  Probabilistic approach to rough sets , 2008, Int. J. Approx. Reason..

[15]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[16]  P. Laplace.  A Philosophical Essay On Probabilities , 1902 .

[17]  Jerzy Stefanowski,et al.  On rough set based approaches to induction of decision rules , 1998 .

[18]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[19]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[20]  D. Gallahan,et al.  False-positive HIV-1 test results in a low-risk screening setting of voluntary blood donation. Retrovirus Epidemiology Donor Study. , 1998, JAMA.

[21]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[22]  Wei Wang,et al.  Traffic Incident Detection Based on Rough Sets Approach , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[23]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[24]  Ralph Weischedel,et al.  PERFORMANCE MEASURES FOR INFORMATION EXTRACTION , 2007 .