A hybrid machine learning technique for complex non-stationary classification problems
Classification problems in machine learning have a wide range of applications including but not limited to, medical imaging, drug discovery, geostatistics, biometric identification, language processing, etc. In general, machine learning algorithms used for classification work on static input data. i...
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sg-ntu-dr.10356-732672023-07-04T17:14:48Z A hybrid machine learning technique for complex non-stationary classification problems Vijaya Krishna Yalavarthi Er Meng Joo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Classification problems in machine learning have a wide range of applications including but not limited to, medical imaging, drug discovery, geostatistics, biometric identification, language processing, etc. In general, machine learning algorithms used for classification work on static input data. i.e. the number of classes in the dataset usually are known a priori or remains constant. In contrast, for several real-life applications, the data are dynamic and non-stationary in nature. The number of target labels is not fixed and can increase in real time. This results in an impending need to develop new machine learning methods to address sequential learning for non-stationary data samples featuring learning parameters. In this project, a novel technique that is independent of the number of class constraints and can adapt to the introduction of new classes it will encounter is developed. The developed technique will enable the system to remodel by itself adapting to dynamic needs of non-stationary input data samples. To be more specific novel machine learning technique based on Extreme Learning Machine is developed. Application of the proposed technique on several benchmark datasets demonstrate that the proposed technique is superior in terms of accuracy and consistency. Master of Engineering 2018-02-06T02:14:32Z 2018-02-06T02:14:32Z 2018 Thesis Vijaya Krishna Yalavarthi. (2018). A hybrid machine learning technique for complex non-stationary classification problems. Master's thesis, Nanyang Technological University, Singapore. http://hdl.handle.net/10356/73267 10.32657/10356/73267 en 72 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Vijaya Krishna Yalavarthi A hybrid machine learning technique for complex non-stationary classification problems |
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Classification problems in machine learning have a wide range of applications including but not limited to, medical imaging, drug discovery, geostatistics, biometric identification, language processing, etc. In general, machine learning algorithms used for classification work on static input data. i.e. the number of classes in the dataset usually are known a priori or remains constant. In contrast, for several real-life applications, the data are dynamic and non-stationary in nature. The number of target labels is not fixed and can increase in real time. This results in an impending need to develop new machine learning methods to address sequential learning for non-stationary data samples featuring learning parameters. In this project, a novel technique that is independent of the number of class constraints and can adapt to the introduction of new classes it will encounter is developed. The developed technique will enable the system to remodel by itself adapting to dynamic needs of non-stationary input data samples. To be more specific novel machine learning technique based on Extreme Learning Machine is developed. Application of the proposed technique on several benchmark datasets demonstrate that the proposed technique is superior in terms of accuracy and consistency. |
author2 |
Er Meng Joo |
author_facet |
Er Meng Joo Vijaya Krishna Yalavarthi |
format |
Theses and Dissertations |
author |
Vijaya Krishna Yalavarthi |
author_sort |
Vijaya Krishna Yalavarthi |
title |
A hybrid machine learning technique for complex non-stationary classification problems |
title_short |
A hybrid machine learning technique for complex non-stationary classification problems |
title_full |
A hybrid machine learning technique for complex non-stationary classification problems |
title_fullStr |
A hybrid machine learning technique for complex non-stationary classification problems |
title_full_unstemmed |
A hybrid machine learning technique for complex non-stationary classification problems |
title_sort |
hybrid machine learning technique for complex non-stationary classification problems |
publishDate |
2018 |
url |
http://hdl.handle.net/10356/73267 |
_version_ |
1772825683757629440 |