A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm
In this paper, we present a Meta-cognitive Interval Type-2 neuro-Fuzzy Inference System (McIT2FIS) classifier and its projection based learning algorithm. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an Interval Type-2...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | other |
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2013
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| Online Access: | https://hdl.handle.net/10356/106697 http://hdl.handle.net/10220/17935 |
| _version_ | 1811680557119045632 |
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| author | Suresh, Sundaram Savitha, R. Subramanian, K. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Suresh, Sundaram Savitha, R. Subramanian, K. |
| author_sort | Suresh, Sundaram |
| collection | NTU |
| description | In this paper, we present a Meta-cognitive Interval Type-2 neuro-Fuzzy Inference System (McIT2FIS) classifier and its projection based learning algorithm. 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. IT2FIS begins with zero rules, and rules are added and updated depending on the relative knowledge represented by the sample in comparison to that represented by the cognitive component. The knowledge representation ability of IT2FIS is controlled by a self-regulatory learning mechanism that forms the meta-cognitive component. 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 (PBL) algorithm uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. The performance of McIT2FIS is evaluated on a set of benchmark classification problems from UCI machine learning repository. The statistical performance comparison with other algorithms available in the literature indicates improved performance of McIT2FIS. |
| first_indexed | 2024-10-01T03:26:56Z |
| format | Conference Paper |
| id | ntu-10356/106697 |
| institution | Nanyang Technological University |
| language | other |
| last_indexed | 2024-10-01T03:26:56Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1066972020-05-28T07:18:10Z A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm Suresh, Sundaram Savitha, R. Subramanian, K. School of Computer Engineering IEEE Conference on Evolving and Adaptive Intelligent Systems (2013 : Singapore) In this paper, we present a Meta-cognitive Interval Type-2 neuro-Fuzzy Inference System (McIT2FIS) classifier and its projection based learning algorithm. 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. IT2FIS begins with zero rules, and rules are added and updated depending on the relative knowledge represented by the sample in comparison to that represented by the cognitive component. The knowledge representation ability of IT2FIS is controlled by a self-regulatory learning mechanism that forms the meta-cognitive component. 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 (PBL) algorithm uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. The performance of McIT2FIS is evaluated on a set of benchmark classification problems from UCI machine learning repository. The statistical performance comparison with other algorithms available in the literature indicates improved performance of McIT2FIS. 2013-11-29T06:36:06Z 2019-12-06T22:16:27Z 2013-11-29T06:36:06Z 2019-12-06T22:16:27Z 2013 2013 Conference Paper Subramanian, K., Savitha, R., & Suresh, S. (2013). A Meta-cognitive Interval Type-2 fuzzy inference system classifier and its projection based learning algorithm. 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 48-55. https://hdl.handle.net/10356/106697 http://hdl.handle.net/10220/17935 10.1109/EAIS.2013.6604104 other |
| spellingShingle | Suresh, Sundaram Savitha, R. Subramanian, K. A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title | A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title_full | A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title_fullStr | A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title_full_unstemmed | A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title_short | A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm |
| title_sort | meta cognitive interval type 2 fuzzy inference system classifier and its projection based learning algorithm |
| url | https://hdl.handle.net/10356/106697 http://hdl.handle.net/10220/17935 |
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