A survey on Improving Classification Accuracy in Data Mining

Authors(2) :-Bhavesh Patankar, Dr. Vijay Chavda

There are various classifiers available for data classification, selecting the best classifier is one of the critical problems of data classification. Also pre-processing approach to be used is quite important. In this paper, study of various approaches to improve the classification accuracy in data mining is carried out. The purpose of the pre-processing is to gain a high degree of distinct classes before the classifier is trained or tested. Handling noise and outliers is an important aspect in data mining to improve the classification accuracy. High accuracy of classification also depends upon the quality of data being used for classification in data mining. Feature selection is also one of the aspects which can refine the dataset before providing it to the learning algorithm to improve the accuracy of the classifier.

Authors and Affiliations

Bhavesh Patankar
Department of M.Sc. (IT), Kadi SarvaVishwaVidyalaya, Gandhinagar, Gujarat, India
Dr. Vijay Chavda
NPCCSM, Kadi SarvaVishwaVidyalaya, Gandhinagar, Gujarat, India

Classification; Pre-processing; Outliers detection; Feature Selection; Dimensionality reduction

  1. Moeinzadeh, H, Nasersharif, B, Rezaee, A., Pazhoumand-dar, H., "Improving Classification Accuracy Using Evolutionary Fuzzy Transformation", 11th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO 2009), Montreal, Canada, 2009 (1)
  2. Bratu, C.V.; Muresan, T.; Potolea, R., "Improving classification accuracy through feature selection," Intelligent Computer Communication and Processing, 2008. ICCP 2008. 4th International Conference on , vol., no., pp.25,32, 28-30 Aug. 2008.
  3. Nilsson,R., Statistical Feature Selection, with Applications in Life Science, PhD Thesis, Linkoping University, 2007.
  4. University, Kohavi, R. Wrappers for Performance Enhancement and Oblivious Decision Graphs, PhD thesis, Stanford University, Computer Science Department, 1995. (3).
  5. Vidrighin C., Potolea R., „Towards a Combined Approach for Feature Selection", accepted at ICSOFT 2008.
  6. T. M. Khoshgoftaar, N. Seliya, and K. Gao. Rule-based noise detection for software measurement data. In Proc.of the IEEE int. conf. on inf. Reuse and Integration,pages 302-307. IEEE Syst., Man, and Cybern. Society, 2004.
  7. Smith, Michael R., and Tony Martinez. "Improving classification accuracy by identifying and removing instances that should be misclassified." Neural Networks (IJCNN), The 2011 International Joint Conference on. IEEE, 2011.
  8. Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining, southeast asia edition: Concepts and techniques. Morgan kaufmann, 2006.

Publication Details

Published in : Volume 1 | Issue 2 | March-April 2015
Date of Publication : 2015-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 535-538
Manuscript Number : IJSRSET151314
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Bhavesh Patankar, Dr. Vijay Chavda, " A survey on Improving Classification Accuracy in Data Mining, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 2, pp.535-538, March-April-2015.
Journal URL : http://ijsrset.com/IJSRSET151314

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