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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Main Author: Vijaya Krishna Yalavarthi
Other Authors: Er Meng Joo
Format: Theses and Dissertations
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/73267
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Vijaya Krishna Yalavarthi
A hybrid machine learning technique for complex non-stationary classification problems
description 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
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