NDLI: Mapping of nearest neighbor for classification

Content Provider	IEEE Xplore Digital Library
Author	Ishii, N. Torii, I. Yongguang Bao Tanaka, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Sci., Aichi Inst. of Technol., Toyota, Japan (Ishii, N.; Torii, I.) \|\| Daido Univ., Nagoya, Japan (Tanaka, H.) \|\| Aichi Inf. Syst., Kariya, Japan (Yongguang Bao)
Abstract	Dimension reduction of data is an important theme in the data processing and on the web to represent and manipulate higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. But, there are problems in the application of reducts for classification. Here, we develop a method which connects reducts and the nearest neighbor method to classify data with higher classification accuracy. To improve the classification ability of reducts, we develop a new graph mapping method of the nearest neighbor based on reducts and weighted modified reducts for the classification with higher accuracy. Then, the mapping method is useful and the weighted modified reduct classifies with higher accuracy.
Starting Page	121
Ending Page	126
File Size	733926
Page Count	6
File Format	PDF
ISBN	9781479901746
DOI	10.1109/ICIS.2013.6607819
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Training Euclidean distance Equations Data analysis dimension reduction classification reduct nearest neighbor
Content Type	Text
Resource Type	Article

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