NDLI: NEVE++: A neuro-evolutionary unlimited ensemble for adaptive learning

Content Provider	IEEE Xplore Digital Library
Author	Escovedo, T. Abs da Cruz, A. Koshiyama, A. Melo, R. Vellasco, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Pontifical Catholic Univ. of Rio de Janeiro (PUC-Rio), Gavea, Brazil (Escovedo, T.; Abs da Cruz, A.; Koshiyama, A.; Melo, R.; Vellasco, M.)
Abstract	In our previous works [1, 2], we proposed NEVE, a model that uses a weighted ensemble of neural network classifiers for adaptive learning, trained by means of a quantum-inspired evolutionary algorithm (QIEA). We showed that the neuro-evolutionary classifiers were able to learn the dataset and to quickly respond to any drifts on the underlying data. Now, we are particularly interested on analyzing the influence of an unlimited ensemble, instead of the limited ensemble from NEVE. For that, we modified NEVE to work with unlimited ensembles, and we call this new algorithm NEVE++. To verity how the unlimited ensemble influences the results, we used four different datasets with concept drift in order to compare the accuracy of NEVE and NEVE++, using two other existing algorithms as reference.
Starting Page	3331
Ending Page	3338
File Size	3718176
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889820
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Evolutionary computation Classification algorithms Training Heuristic algorithms Data models Accuracy Quantum-Inspired Evolution Concept Drift Adaptive Learning Nonstationary Environments Neuro-Evolutionary Ensemble
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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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