A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection
Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of rese...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8951014/ |
| _version_ | 1818665332973240320 |
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| author | Irfan Khan Xianchao Zhang Mobashar Rehman Rahman Ali |
| author_facet | Irfan Khan Xianchao Zhang Mobashar Rehman Rahman Ali |
| author_sort | Irfan Khan |
| collection | DOAJ |
| description | Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction. |
| first_indexed | 2024-12-17T05:46:58Z |
| format | Article |
| id | doaj.art-e10bd3a9a5da4450986e0b6adaea33d8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:46:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e10bd3a9a5da4450986e0b6adaea33d82022-12-21T22:01:18ZengIEEEIEEE Access2169-35362020-01-018102621028110.1109/ACCESS.2020.29647268951014A Literature Survey and Empirical Study of Meta-Learning for Classifier SelectionIrfan Khan0https://orcid.org/0000-0001-6964-4523Xianchao Zhang1Mobashar Rehman2https://orcid.org/0000-0003-1182-2504Rahman Ali3School of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaDepartment of Information Systems, Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, MalaysiaQACC, University of Peshawar, Peshawar, PakistanClassification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.https://ieeexplore.ieee.org/document/8951014/Meta-learningalgorithm selectionclassificationmachine learning |
| spellingShingle | Irfan Khan Xianchao Zhang Mobashar Rehman Rahman Ali A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection IEEE Access Meta-learning algorithm selection classification machine learning |
| title | A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection |
| title_full | A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection |
| title_fullStr | A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection |
| title_full_unstemmed | A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection |
| title_short | A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection |
| title_sort | literature survey and empirical study of meta learning for classifier selection |
| topic | Meta-learning algorithm selection classification machine learning |
| url | https://ieeexplore.ieee.org/document/8951014/ |
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