Presentation 2000/7/7
Multiple k-Nearest Neighbor Classifier Using Test Features
Itqon I, Shun'ichi Kaneko, Satoru Igarashi,
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Abstract(in English) We propose TFkNN which is an introduction of test features to classifier combination of multiple kNNs with the aim of improving classification performance. In TFkNN, an unknown pattern is classified on each test feature by the k-nearest neighbor rule. Scores from each local classifier are then collected to decide final discrimination. We performed simulation to show that test features are effective subspaces. The proposed classifier was then demonstrated on artificial data having several overlapping conditions. The experimental results show that TFkNN can give better performance than kNN. TFkNN moreover can also stabilize the recognition rate from the different parameter κ. Altough we implemented TFkNN in series, it requires the recognition time not so more than the single kNN. We also applied TFkNN to a real character recognition, resulting a high performance.
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Keyword(in English) pattern recognition / test feature / k-nearest neighbor / voting / classifier combination
Paper # IE2000-30,PRMU2000-55,MVE2000-59
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Committee IE
Conference Date 2000/7/7(1days)
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Registration To Image Engineering (IE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Multiple k-Nearest Neighbor Classifier Using Test Features
Sub Title (in English)
Keyword(1) pattern recognition
Keyword(2) test feature
Keyword(3) k-nearest neighbor
Keyword(4) voting
Keyword(5) classifier combination
1st Author's Name Itqon I
1st Author's Affiliation Graduate School of Engineering, Hokkaido University()
2nd Author's Name Shun'ichi Kaneko
2nd Author's Affiliation Graduate School of Engineering, Hokkaido University
3rd Author's Name Satoru Igarashi
3rd Author's Affiliation Graduate School of Engineering, Hokkaido University
Date 2000/7/7
Paper # IE2000-30,PRMU2000-55,MVE2000-59
Volume (vol) vol.100
Number (no) 180
Page pp.pp.-
#Pages 8
Date of Issue