An effective refinement strategy for KNN text classifier Export

@article{nn_knn_text_classifier, abstract = {Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. A wide range of supervised learning algorithms has been introduced to deal with text classification. Among all these classifiers, K-Nearest Neighbors (KNN) is a widely used classifier in text categorization community because of its simplicity and efficiency. However, KNN still suffers from inductive biases or model misfits that result from its assumptions, such as the presumption that training data are evenly distributed among all categories. In this paper, we propose a new refinement strategy, which we called as DragPushing, for the KNN Classifier. The experiments on three benchmark evaluation collections show that DragPushing achieved a significant improvement on the performance of the KNN Classifier.}, author = {Tan, Songbo}, citeulike-article-id = {1267213}, citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V03-4GV9B47-3/2/fc87800b02eebdc91dabac2fa71cc4f2}, journal = {Expert Systems with Applications}, keywords = {diplomarbeit, information\_retrieval, knn, ml}, month = {February}, number = {2}, pages = {290--298}, posted-at = {2007-08-11 12:11:39}, priority = {3}, title = {An effective refinement strategy for KNN text classifier}, url = {http://www.sciencedirect.com/science/article/B6V03-4GV9B47-3/2/fc87800b02eebdc91dabac2fa71cc4f2}, volume = {30}, year = {2006} }

TY - JOUR ID - nn_knn_text_classifier L3 - citeulike-article-id:1267213 N2 - Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. A wide range of supervised learning algorithms has been introduced to deal with text classification. Among all these classifiers, K-Nearest Neighbors (KNN) is a widely used classifier in text categorization community because of its simplicity and efficiency. However, KNN still suffers from inductive biases or model misfits that result from its assumptions, such as the presumption that training data are evenly distributed among all categories. In this paper, we propose a new refinement strategy, which we called as DragPushing, for the KNN Classifier. The experiments on three benchmark evaluation collections show that DragPushing achieved a significant improvement on the performance of the KNN Classifier. IS - 2 JF - Expert Systems with Applications EP - 298 TI - An effective refinement strategy for KNN text classifier VL - 30 SP - 290 KW - diplomarbeit KW - information_retrieval KW - knn KW - ml AU - Tan, Songbo PY - 2006/02// UR - http://www.sciencedirect.com/science/article/B6V03-4GV9B47-3/2/fc87800b02eebdc91dabac2fa71cc4f2 ER -