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.

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

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