Performance Comparative in Classification
Algorithms Using Real Datasets |
Hanuman Thota1,2, Raghava Naidu Miriyala1,2, Siva Prasad Akula2,5,
K.Mrithyunjaya Rao3, Chandra Sekhar Vellanki 1,Allam Appa Rao4,Srinubabu Gedela*4,5 |
| 1D.M.S S.V.H. College of Engineering, Department of Computer
Science, Machilipatnam-521002, India |
| 22Department of Computer Science and Engineering, Acharya
Nagarjuna University, Guntur-522510, India |
| 33Vaagdevi College of Engineering, Warangal-506005, India |
| 44International Center for Bioinformatics, Department of Computer Science
and Systems Engineering , Andhra University, Visakhapatnam-530003, India |
| 55Institute 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,
E-mail: srinubabuau6@gmail.com |
|
| 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: ©2008 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. |
| 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. |
|
|
|