Improving kNN Text Categorization by Removing Outliers from Training Set

We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method--Centroid-based classifier. Outliers are the elements whose similarity to the centroid of the corresponding category is below a threshold.

• Type: Conference
• Year: 2006
• Where: CICLING
• Authors: Kwangcheol Shin, Ajith Abraham, Sang-Yong Han
• Keywords: CICLING 2006, KNN Classifier, Natural Language Processing, Outliers, Training Data