A meta-cognitive learning algorithm for an extreme learning machine classifier
This paper presents an efficient fast learning classifier based on the Nelson and Narens model of human meta-cognition, namely ‘Meta-cognitive Extreme Learning Machine (McELM).’ McELM has two components: a cognitive component and a meta-cognitive component. The cognitive component of McELM is a thre...
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| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/101260 http://hdl.handle.net/10220/16777 |
| _version_ | 1811676356140859392 |
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| author | Suresh, Sundaram Savitha, R. Kim, H. J. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Suresh, Sundaram Savitha, R. Kim, H. J. |
| author_sort | Suresh, Sundaram |
| collection | NTU |
| description | This paper presents an efficient fast learning classifier based on the Nelson and Narens model of human meta-cognition, namely ‘Meta-cognitive Extreme Learning Machine (McELM).’ McELM has two components: a cognitive component and a meta-cognitive component. The cognitive component of McELM is a three-layered extreme learning machine (ELM) classifier. The neurons in the hidden layer of the cognitive component employ the q-Gaussian activation function, while the neurons in the input and output layers are linear. The meta-cognitive component of McELM has a self-regulatory learning mechanism that decides what-to-learn, when-to-learn, and how-to-learn in a meta-cognitive framework. As the samples in the training set are presented one-by-one, the meta-cognitive component receives the monitory signals from the cognitive component and chooses suitable learning strategies for the sample. Thus, it either deletes the sample, uses the sample to add a new neuron, or updates the output weights based on the sample, or reserves the sample for future use. Therefore, unlike the conventional ELM, the architecture of McELM is not fixed a priori, instead, the network is built during the training process. While adding a neuron, McELM chooses the centers based on the sample, and the width of the Gaussian function is chosen randomly. The output weights are estimated using the least square estimate based on the hinge-loss error function. The hinge-loss error function facilitates prediction of posterior probabilities better than the mean-square error and is hence preferred to develop the McELM classifier. While updating the network parameters, the output weights are updated using a recursive least square estimate. The performance of McELM is evaluated on a set of benchmark classification problems from the UCI machine learning repository. Performance study results highlight that meta-cognition in ELM framework enhances the decision-making ability of ELM significantly. |
| first_indexed | 2024-10-01T02:20:10Z |
| format | Journal Article |
| id | ntu-10356/101260 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:20:10Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1012602020-05-28T07:18:10Z A meta-cognitive learning algorithm for an extreme learning machine classifier Suresh, Sundaram Savitha, R. Kim, H. J. School of Computer Engineering DRNTU::Engineering::Computer science and engineering This paper presents an efficient fast learning classifier based on the Nelson and Narens model of human meta-cognition, namely ‘Meta-cognitive Extreme Learning Machine (McELM).’ McELM has two components: a cognitive component and a meta-cognitive component. The cognitive component of McELM is a three-layered extreme learning machine (ELM) classifier. The neurons in the hidden layer of the cognitive component employ the q-Gaussian activation function, while the neurons in the input and output layers are linear. The meta-cognitive component of McELM has a self-regulatory learning mechanism that decides what-to-learn, when-to-learn, and how-to-learn in a meta-cognitive framework. As the samples in the training set are presented one-by-one, the meta-cognitive component receives the monitory signals from the cognitive component and chooses suitable learning strategies for the sample. Thus, it either deletes the sample, uses the sample to add a new neuron, or updates the output weights based on the sample, or reserves the sample for future use. Therefore, unlike the conventional ELM, the architecture of McELM is not fixed a priori, instead, the network is built during the training process. While adding a neuron, McELM chooses the centers based on the sample, and the width of the Gaussian function is chosen randomly. The output weights are estimated using the least square estimate based on the hinge-loss error function. The hinge-loss error function facilitates prediction of posterior probabilities better than the mean-square error and is hence preferred to develop the McELM classifier. While updating the network parameters, the output weights are updated using a recursive least square estimate. The performance of McELM is evaluated on a set of benchmark classification problems from the UCI machine learning repository. Performance study results highlight that meta-cognition in ELM framework enhances the decision-making ability of ELM significantly. 2013-10-24T06:58:47Z 2019-12-06T20:35:46Z 2013-10-24T06:58:47Z 2019-12-06T20:35:46Z 2013 2013 Journal Article Savitha, R., Suresh, S., & Kim, H. J.(2013). A meta-cognitive learning algorithm for an extreme learning machine classifier. Cognitive computation, 6(2), 253-263. 1866-9956 https://hdl.handle.net/10356/101260 http://hdl.handle.net/10220/16777 10.1007/s12559-013-9223-2 en Cognitive computation |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Suresh, Sundaram Savitha, R. Kim, H. J. A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title | A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title_full | A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title_fullStr | A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title_full_unstemmed | A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title_short | A meta-cognitive learning algorithm for an extreme learning machine classifier |
| title_sort | meta cognitive learning algorithm for an extreme learning machine classifier |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/101260 http://hdl.handle.net/10220/16777 |
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