NDLI: A Meta-cognitive Fully Complex-valued Fast Learning Classifier for real-valued classification problems

Content Provider	IEEE Xplore Digital Library
Author	Sivachitra, M. Vijayachitra, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. & Instrum. Eng., Kongu Eng. Coll., Perundurai, India (Vijayachitra, S.) \|\| Dept. of Electr. & Electron. Eng, Kongu Eng. Coll., Perundurai, India (Sivachitra, M.)
Abstract	In this article, a Meta-cognitive Fully Complex-valued Fast Learning Classifier (Mc-FCFLC) for solving real-valued classification problems is presented. Mc-FCFLC consist of two components namely, a cognitive component and a meta-cognitive one. The cognitive component of Mc-FCFLC is a single hidden layer network (FCFLC) with a nonlinear input and hidden layer, and a linear output layer. The meta-cognitive component of Mc-FCFLC consist of a self-regulatory learning mechanism that chooses a best learning strategy among what-to-learn, when-to-learn and how-to-learn for a given sample. The sample is either deleted, used for adding a new neuron or else it is reserved for future use. Thus the architecture of Mc-FCFLC is constructed during the training process. The performance of the Mc-FCFLC is evaluated with the other complex-valued and a few best performing real-valued classifiers on a set of benchmark classification problems obtained from the UCI machine learning repository. Further, a practical acoustic emission signal classification problem has been addressed. Performance results demonstrate that Mc-FCFLC has better classification ability than the other classifiers existing in the literature.
Starting Page	1
Ending Page	6
File Size	110569
Page Count	6
File Format	PDF
ISBN	9781479971718
DOI	10.1109/CCIP.2015.7100712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-03
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Neurons Benchmark testing Acoustic emission Brain modeling
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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