NDLI: Self-organizing neural grove

Content Provider	IEEE Xplore Digital Library
Author	Inoue, H. Sugiyama, K.
Copyright Year	2013
Description	Author affiliation: Adv. Eng. Fac., Kure Nat. Coll. of Technol., Hiroshima, Japan (Sugiyama, K.) \|\| Dept. of Electr. Eng. & Comput. Sci., Kure Nat. Coll. of Technol., Hiroshima, Japan (Inoue, H.)
Abstract	Recently, multiple classifier systems have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on SGNN increases in proportion to the numbers of SGNN. In this paper, we propose a novel pruning method for efficient classification and we call this model a self-organizing neural grove (SONG). Experiments have been conducted to compare the SONG with bagging and the SONG with boosting, the multiple classifier system based on C4.5, and support vector machine (SVM). The results show that the SONG can improve its classification accuracy as well as reducing the computation cost.
Starting Page	319
Ending Page	323
File Size	132385
Page Count	5
File Format	PDF
ISBN	9781479914265
e-ISBN	9781479914296
DOI	10.1109/IDAACS.2013.6662697
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-12
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Support vector machines Bagging Memory management Boosting Neural networks Data mining boosting neural network ensembles self-organization bagging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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