Meta-cognitive neural network for classification problems in a sequential learning framework
In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles. The network, referred to as Meta-cognitive Neural Network (McNN). McNN has two components, namely the cognitive component and the meta-cognitive component. A r...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/98781 http://hdl.handle.net/10220/13658 |
| _version_ | 1826112503933829120 |
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| author | Sateesh Babu, Giduthuri Suresh, Sundaram |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Sateesh Babu, Giduthuri Suresh, Sundaram |
| author_sort | Sateesh Babu, Giduthuri |
| collection | NTU |
| description | In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles. The network, referred to as Meta-cognitive Neural Network (McNN). McNN has two components, namely the cognitive component and the meta-cognitive component. A radial basis function network is the fundamental building block of the cognitive component. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. When a sample is presented at the cognitive component of McNN, the meta-cognitive component chooses the best learning strategy for the sample using estimated class label, maximum hinge error, confidence of classifier and class-wise significance. Also sample overlapping conditions are considered in growth strategy for proper initialization of new hidden neurons. The performance of McNN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository and two practical problems, viz., the acoustic emission for signal classification and a mammogram data set for cancer classification. The statistical comparison clearly indicates the superior performance of McNN over reported results in the literature. |
| first_indexed | 2024-10-01T03:08:07Z |
| format | Journal Article |
| id | ntu-10356/98781 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:08:07Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/987812020-05-28T07:18:10Z Meta-cognitive neural network for classification problems in a sequential learning framework Sateesh Babu, Giduthuri Suresh, Sundaram School of Computer Engineering DRNTU::Engineering::Computer science and engineering In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles. The network, referred to as Meta-cognitive Neural Network (McNN). McNN has two components, namely the cognitive component and the meta-cognitive component. A radial basis function network is the fundamental building block of the cognitive component. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. When a sample is presented at the cognitive component of McNN, the meta-cognitive component chooses the best learning strategy for the sample using estimated class label, maximum hinge error, confidence of classifier and class-wise significance. Also sample overlapping conditions are considered in growth strategy for proper initialization of new hidden neurons. The performance of McNN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository and two practical problems, viz., the acoustic emission for signal classification and a mammogram data set for cancer classification. The statistical comparison clearly indicates the superior performance of McNN over reported results in the literature. 2013-09-24T07:28:18Z 2019-12-06T19:59:36Z 2013-09-24T07:28:18Z 2019-12-06T19:59:36Z 2011 2011 Journal Article Sateesh Babu, G., & Suresh, S. (2011). Meta-cognitive neural network for classification problems in a sequential learning framework. Neurocomputing, 81, 86-96. https://hdl.handle.net/10356/98781 http://hdl.handle.net/10220/13658 10.1016/j.neucom.2011.12.001 en Neurocomputing |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Sateesh Babu, Giduthuri Suresh, Sundaram Meta-cognitive neural network for classification problems in a sequential learning framework |
| title | Meta-cognitive neural network for classification problems in a sequential learning framework |
| title_full | Meta-cognitive neural network for classification problems in a sequential learning framework |
| title_fullStr | Meta-cognitive neural network for classification problems in a sequential learning framework |
| title_full_unstemmed | Meta-cognitive neural network for classification problems in a sequential learning framework |
| title_short | Meta-cognitive neural network for classification problems in a sequential learning framework |
| title_sort | meta cognitive neural network for classification problems in a sequential learning framework |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98781 http://hdl.handle.net/10220/13658 |
| work_keys_str_mv | AT sateeshbabugiduthuri metacognitiveneuralnetworkforclassificationproblemsinasequentiallearningframework AT sureshsundaram metacognitiveneuralnetworkforclassificationproblemsinasequentiallearningframework |