Bagging and Boosting Negatively Correlated Neural Networks

Material type: 文書・図像類

Author: ISLAM, Md. Monirulほか

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Publication date: 2008-06

Material Format: Digital

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Note (General)：: In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The p...

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Material Type: 文書・図像類
Title: Bagging and Boosting Negatively Correlated Neural Networks
Author/Editor: ISLAM, Md. Monirul
YAO, Xin
NIRJON, S. M. Shahriar
ISLAM, Muhammad Asiful
MURASE, Kazuyuki
Author Heading: ISLAM, Md. Monirul
YAO, Xin
NIRJON, S. M. Shahriar
ISLAM, Muhammad Asiful
MURASE, Kazuyuki
Publication, Distribution, etc.: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Date: 2008-06
Publication Date (W3CDTF): 2008-06
Periodical title: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B -CYBERNETICS
No. or year of volume/issue: 38 3
Volume: 38
Issue: 3
Pages: 771-784
ISSN (Periodical Title): ISSN : 1083-4419
Text Language Code: eng
Subject Heading: Bagging
boosting
constructive approach
diversity
generalization
negative correlation learning
neural network (NN) ensemble design
Target Audience: 一般
Note (General): In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting algorithms are used in NegBagg and NegBoost, respectively, to create different training sets for different NNs in the ensemble. The idea behind using negative correlation learning in conjunction with the bagging/boosting algorithm is to facilitate interaction and cooperation among NNs during their training. Both NegBagg and NegBoost use a constructive approach to automatically determine the number of hidden neurons for NNs. NegBoost also uses the constructive approach to automatically determine the number of NNs for the ensemble. The two algorithms have been tested on a number of benchmark problems in machine learning and NNs, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, satellite, soybean, and waveform problems. The experimental results show that Neg- Bagg and NegBoost require a small number of training epochs to produce compact NN ensembles with good generalization.
Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=23024&item_no=1&attribute_id=22&file_no=1
https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=23024&item_no=1&attribute_id=22&file_no=1
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ