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Title: An Extensive Experimental Study on the Cluster-based Reference Set Reduction for Speeding-up the k-NN Classifier
Authors: Ougiaroglou, Stefanos
Evangelidis, Georgios
Dervos, Dimitris A.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Clustering
Data Reduction
Subjects MESH: K-NN classification
Issue Date: 2012
First Page: 12
Last Page: 15
Volume Title: International Conference on Integrated Information 2011, Island of Kos, Greece
Abstract: The k-Nearest Neighbor (k-NN) classification algorithm is one of the most widely-used lazy classifiers because of its simplicity and ease of implementation. It is considered to be an effective classifier and has many applications. However, its ma- jor drawback is that when sequential search is used to find the neighbors, it involves high computational cost. Speeding-up k-NN search is still an active research field. Hwang and Cho have recently proposed an adaptive cluster-based method for fast Nearest Neigh- bor searching. The effectiveness of this method is based on the adjustment of three parameters. However, the authors evaluated their method by setting specific pa- rameter values and using only one dataset. In this pa- per, an extensive experimental study of this method is presented. The results, which are based on five real life datasets, illustrate that if the parameters of the method are carefully defined, one can achieve even better clas- sification performance
Electronic ISSN: 2623-4629
Appears in Collections:Department of Applied Informatics

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