"INCREASING ACCURACY OF K-NEAREST NEIGHBOR CLASSIFIER FOR TEXT CLASSIFI" by FALGUNI N. PATEL

International Journal of Computer Science and Informatics

Article Title

INCREASING ACCURACY OF K-NEAREST NEIGHBOR CLASSIFIER FOR TEXT CLASSIFICATION

Authors

FALGUNI N. PATEL, Computer Engineering Department, Gujarat Technical University, Sardar Vallabhbhai Patel Institute Of Technology, Vasad, Dist-Anand, GujaratFollow

Abstract

k - Nearest Neighbor Rule is a well-known technique for text classification. The reason behind this is its simplicity, effectiveness, easily modifiable. In this paper, we briefly discuss text classification, k-NN algorithm and analyse the sensitivity problem of k value. To overcome this problem, we introduced inverse cosine distance weighted voting function for text classification. Therefore, Accuracy of text classification is increased even if any large value for k is chosen, as compared to simple k Nearest Neighbor classifier. The proposed weighted function is proved as more effective when any application has large text dataset with some dominating categories, using experimental results.

Recommended Citation

PATEL, FALGUNI N. (2014) "INCREASING ACCURACY OF K-NEAREST NEIGHBOR CLASSIFIER FOR TEXT CLASSIFICATION," International Journal of Computer Science and Informatics: Vol. 4: Iss. 2, Article 7.
DOI: 10.47893/IJCSI.2014.1183
Available at: https://www.interscience.in/ijcsi/vol4/iss2/7

DOI

10.47893/IJCSI.2014.1183

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