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Performance Comparative in Classification Algorithms Using Real Datasets | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Performance Comparative in Classification Algorithms Using Real Datasets

Hanuman Thota1,2, Raghava Naidu Miriyala1,2, Siva Prasad Akula 2,5 , K.Mrithyunjaya Rao3 , Chandra Sekhar Vellanki1 ,Allam Appa Rao4, Srinubabu Gedela*4,5
1D.M.S S.V.H. College of Engineering, Department of Computer Science, Machilipatnam-521002, India
2Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur-522510, India
3Vaagdevi College of Engineering, Warangal-506005, India
4International Center for Bioinformatics, Department of Computer Science and Systems Engineering , Andhra   University, Visakhapatnam-530003, India
5Institute of Glycoproteomics & Systems Biology, Tarnaka, Hyderbad-500017, India
Corresponding Author : Dr. Srinubabu Gedela
Institute of Glycoproteomics & Systems Biology,
Tarnaka, Hyderbad-500017, India
Phone : +91-40-27006539,
Fax      : +91-40-40131662,
Email  : [email protected]
Received December 13, 2008; Accepted February 08, 2009; Published February 23, 2009
Citation: Hanuman T, Raghava NM, Siva PA, Mrithyunjaya RK , Chandra SV, et al. (2009) Performance Comparative in Classification Algorithms Using Real Datasets. J Comput Sci Syst Biol 2:097-100. doi:10.4172/jcsb.1000021
Copyright: © 2009 Hanuman T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Classification is one of the most common data mining tasks, used frequently for data categorization and analysis in the industry and research. In real-world data mining sometimes it mainly deals with noisy information sources, because of data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made perturbations frequently result in imprecise or vague data which is called as noisy data. This noisy data may decrease performance of any classification algorithms. This paper deals with the performance of different classification algorithms and the impact of feature selection algorithm on Logistic Regression Classifier, How it controls False Discovery Rate (FDR) and thus improves the efficiency of Logistic Regression classifier.

Keywords

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