Solve Using R Lanhuage :

Meta-data Data Set Download Data Foldec Data Set Description Abstract: Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project). Source: Creator LIACC-University of Porto R.Campo Alegre 823 4150 PORTO Donor: PB. Brazdil or J. Gama LACCC, University of Porto Rua Campo Aiegre 823 4150 Porto, Portugal Tel. +3516001672 Fax: +3516003654 Email statlog-adm: 1 Cotncc ure.t Data Set Information: This DataSet is about the results of Statiog project. The project performed a comparative study between Statistical, Neural and Symbolic learning algorithms. Project Statt og (Esprit Project 5170) was concerned with comparative studies of different machine learning , neural and statistical classification algorithme. About 20 different algorithms were evaluated on more than 20 different datasets. The tests carried out under project produced many interesting results. The results of these tests are comprehensively described in a book (DMichie et al, 1994). Attribute information: 1. DS. Name categorical Name of DataSet 2. T continuous Number of examples in test set 3. N continuous Number of examples 4. p continuous Number of attributes 5. k continuous Number of classes 6. Bin continuous Number of binary Attributes 7. Cost continuous Cost ( (18yes,0 non) 8. SDratio continuous Standard deviation ratio 9. correl continuous Mean correlation between attributes 10. cancon continuous First canonical correlation 11. cancor2 continuous Second canonical correlation 12. fract1 continuous First eigenwalue 13. fract2 continuous Second eigenvalue 14. akewness continuous Mean of I E(X-Mean)/ 3/STD3 15. kurtosis continuous Mean of IE(XMMean)/4/STD 4 16. He continuous Mean entropy of attributes 17. Hx continuous Entropy of classes 18. MCx continuous Mean mutual entropy of class and attributes 19. EnAtr continuous Equivalent number of attributes 20. NSRatio continuous Noise-signal ratio 21. Alg. Name categorical Name of Algorithm 22. Normerror continuous Normakzed Error (continuous class) Relevant Papers: 'Machine Learning, Neural and Statistical Leaming". Eds. D. Michie.D. J Spiegelhalter and C. Taylor Ellis Horwood-1994 PBrazdil, J.Gama and B. Henery 'Characterizing the Applicabilsy of Classification Alporithms Using Meta-Level Learning" in Proc of Machine Leaming- ECML-94. ed. FBergadano and L.de Raedt.LNAI Vol.784 Springer-Verlag [Web Links JJama, PBrazdil. 'Characterization of Classification Aigorithms' in Proc, of EPIA 95, LNAI Vol. 990 Springer-Verlag, 1995 [Web Linki Citation Request: Please reter to the Machine Learning Repositorys sitation polisx