NDLI: A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

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Controlling maximum evaluation duration in on-line and on-board evolutionary robotics : On-the-fly control of maximum evaluation duration in on-line and on-board evolutionary robotics in highly dynamic and uncertain environments

Evolving Systems : Volume 5, Issue 3, September 2014

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A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

Content Provider	Springer Nature Link
Author	Subramanian, Kartick Das, Ankit Kumar Sundaram, Suresh Ramasamy, Savitha
Copyright Year	2013
Abstract	A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an interval type-2 neuro-fuzzy inference system (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. A self-regulatory learning mechanism forms the meta-cognitive component. IT2FIS begins with zero rules, and rules are added and updated depending on the prediction error and relative knowledge contained the current sample. As each sample is presented to the network, the meta-cognitive component monitors the hinge-loss error and class-specific spherical potential of the current sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a projection based learning algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. It uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. Moreover, consistently under - performing rules are pruned from the network leading to a compact network. The performance of McIT2FIS is first evaluated on a set of benchmark classification problems from UCI machine learning repository. A tenfold cross validation based performance comparison with other state-of-the-art approaches indicates its improved performance. Next, its performance is evaluated on detection of attention deficiency hyperactivity disorder (ADHD) in children. The aim of this study is to classify a child as having typically developing controls or as an ADHD patient. Voxel based features extracted from amygdala region of the brain is employed in this study. The network is trained and tested on samples obtained from ADHD-200 consortium dataset consisting of 941 subjects. The performance comparison with standard support vector machine shows that McIT2FIS has superior classification ability than SVM in diagnosing ADHD.
Starting Page	219
Ending Page	230
Page Count	12
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	5
Issue Number	4
e-ISSN	18686486
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-12-31
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Interval type-2 fuzzy systems Meta-cognition Self-regulation Attention deficiency hyperactivity disorder Projection based learning Classification Complexity Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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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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