Combined Approach for Improving Accuracy of Prototype Selection for k-NN Classifier

Authors

  • Gadodiya S M.Tech Student, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
  • Chandak M Professor, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

Keywords:

k-nearest neighbor classifier, data reduction, prototype selection, efficiency

Abstract

The k-nearest-neighbour classifier is a powerful tool for multiclass classification and thus widely used in data mining techniques. But it consists of some severe drawbacks: high storage requirements, low noise tolerance and low efficiency in classification response. The solution to these drawbacks is to apply nearest neighbor on the reduced dataset which can be obtained by applying Prototype Selection methods on original training dataset. Various Prototype Selection methods have been developed yet but are not that efficient to overcome all the drawbacks simultaneously. So here is an attempt to build relatively more efficient algorithm by combining two or three previously developed approaches.

References

http://www.inf.ed.ac.uk/teaching/courses/dme/htm l/datasets0405.html for dataset used in the experiment.

Shikha V. Gadodiya, Manoj B. Chandak, 2013 Prototype Selection Algorithms for kNN Classifier: A Survey in International Journal of Advanced Research in Computer and Communication Engineering Vol.2, Issue 12, December 2013

Garc_IAET AL 2012 , Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 3, March 2012.

Salvador Garc´ıa, Joaqu´ın Derrac, Jos´e Ram´on Cano, and Francisco Herrera, .PROTOTYPE SELECTION FOR NEAREST NEIGHBOR CLASSIFICATION: SURVEY OF METHODS.

K. Hattori and M. Takahashi 2000, A new edited k-nearest neighbor rule in the pattern classification problem, Pattern Recognition, vol. 33,no. 3, pp. 521–528, 2000.

J. ALCALÁ-FDEZ, A. FERNÁNDEZ, J. LUENGO, J. DERRAC,S. GARCÍA, L. SÁNCHEZ AND F. HERRERA , KEEL DataMining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework in J. of Mult.-Valued Logic & Soft Computing, Vol. 17, pp. 255–287.

http://sci2s.ugr.es/pgtax/experimentation.php#sum m1m to get KEEL software tool.

N. Jankowski and M. Grochowski 2004, Comparison of Instances Selection Algorithms I. Algorithms Survey,Proc. Int’l Conf. Artificial

Intelligence and Soft Computing, pp. 598-603, 2004.

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Published

2024-02-26

How to Cite

Gadodiya, S., & Chandak, M. B. (2024). Combined Approach for Improving Accuracy of Prototype Selection for k-NN Classifier. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(05), 808–811. Retrieved from https://ijact.in/index.php/j/article/view/143

Issue

Section

Original Research Article

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