Title :
Combining KNN algorithm and other classifiers
Author :
Yan, Zhiyong ; Xu, Congfu
Author_Institution :
Dept. of Comput. Sci., Zhejiang Univ., Hangzhou, China
Abstract :
In this paper, we propose KNC algorithm for combining KNN algorithm and other three classifiers (C4.5 algorithm, Naive Bayes classifier and SVM) based on their classification capabilities on different types of instances. According to labels of instances and their K nearest neighbors, we divide instances into three types, S-, DS- and D-type. The classification capabilities of KNN algorithm on S-type instances are the best, while ones on D-type and DS-type are usually worse than other three classifiers. KNC algorithm uses KNN algorithm to classify S- and DS-type instances, and uses other classifiers to classify D-type instances. KNC algorithm utilizes classification capability of KNN algorithm on S-type instances and utilizes classification capabilities of other three classifiers on D-type instances. Experimental results on 20 UCI data sets demonstrate utility and feasibility of KNC algorithm.
Keywords :
pattern classification; support vector machines; C4.5 algorithm; D-type classification; DS-type classification; KNN algorithm; Naive Bayes classifier; S-type classification; SVM; k-nearest neighbor algorithm; Accuracy; Artificial neural networks; Classification algorithms; Nearest neighbor searches; Niobium; Prediction algorithms; Support vector machines; C4.5 Algorithm; Ensemble learning; KNN algorithm; Naive Bayes Classifier; SVM;
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
DOI :
10.1109/COGINF.2010.5599804