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