NDLI: Bayesian Citation-KNN with distance weighting

Content Provider	Springer Nature Link
Author	Jiang, Liangxiao Cai, Zhihua Wang, Dianhong Zhang, Harry
Copyright Year	2013
Abstract	Multi-instance (MI) learning is receiving growing attention in the machine learning research field, in which learning examples are represented by a bag of instances instead of a single instance. K-nearest-neighbor (KNN) is a simple and effective classification model in the traditional supervised learning. As its two variants, Bayesian-KNN (BKNN) and Citation-KNN (CKNN) are proposed and are widely used for solving multi-instance classification problems. However, CKNN still applies the simplest majority vote approach among the references and citers to classify unseen bags. In this paper, we propose an improved algorithm called Bayesian Citation-KNN (BCKNN). For each unseen bag, BCKNN firstly finds its $$ k $$ references and $$ q $$ citers respectively, and then a Bayesian approach is applied to its $$ k $$ references and a distance weighted majority vote approach is applied to its $$ q $$ citers. The experimental results on several benchmark datasets show that our BCKNN is generally better than previous BKNN and CKNN. Besides, BCKNN almost maintains the same order of computational overhead as CKNN.
Starting Page	193
Ending Page	199
Page Count	7
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	5
Issue Number	2
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-02-12
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-instance learning KNN Bayesian-KNN Citation-KNN Bayesian Citation-KNN Distance weighting Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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