Representing uncertainty and imprecision in machine learning: A survey on belief functions
Uncertainty and imprecision accompany the world we live in and occur in almost every event. How to better interpret and manage uncertainty and imprecision play a vital role in machine learning (ML). As an effective tool for modeling imperfection, the theory of belief functions (TBF) has attracted su...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823004585 |
| _version_ | 1797322427918712832 |
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| author | Zhe Liu Sukumar Letchmunan |
| author_facet | Zhe Liu Sukumar Letchmunan |
| author_sort | Zhe Liu |
| collection | DOAJ |
| description | Uncertainty and imprecision accompany the world we live in and occur in almost every event. How to better interpret and manage uncertainty and imprecision play a vital role in machine learning (ML). As an effective tool for modeling imperfection, the theory of belief functions (TBF) has attracted substantial attention by providing a flexible discernment of framework for effectively representing uncertainty and imprecision. To date, many TBF-based methods have been proposed in ML, but they have not yet been comprehensively summarized. This paper surveys TBF-based methods for representing uncertainty and imprecision in ML, focusing on clustering, classification and information fusion. First, we provide a formal definition of uncertainty and imprecision reasoning. On this basis, we survey the existing TBF-based methods in detail and explain how to characterize uncertainty and imprecision in the results. What is more, we discuss the current challenges in TBF-based ML and offer insightful perspectives for future research regarding clustering, classification and information fusion. This survey not only fills a critical gap in the existing literature but also serves as a guiding beacon for future explorations, emphasizing the transformative role of TBF in advancing ML methodologies. |
| first_indexed | 2024-03-08T05:14:10Z |
| format | Article |
| id | doaj.art-749f371f13d746278b4fa3611101af0f |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-08T05:14:10Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-749f371f13d746278b4fa3611101af0f2024-02-07T04:43:10ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782024-01-01361101904Representing uncertainty and imprecision in machine learning: A survey on belief functionsZhe Liu0Sukumar Letchmunan1Corresponding authors.; School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, MalaysiaCorresponding authors.; School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, MalaysiaUncertainty and imprecision accompany the world we live in and occur in almost every event. How to better interpret and manage uncertainty and imprecision play a vital role in machine learning (ML). As an effective tool for modeling imperfection, the theory of belief functions (TBF) has attracted substantial attention by providing a flexible discernment of framework for effectively representing uncertainty and imprecision. To date, many TBF-based methods have been proposed in ML, but they have not yet been comprehensively summarized. This paper surveys TBF-based methods for representing uncertainty and imprecision in ML, focusing on clustering, classification and information fusion. First, we provide a formal definition of uncertainty and imprecision reasoning. On this basis, we survey the existing TBF-based methods in detail and explain how to characterize uncertainty and imprecision in the results. What is more, we discuss the current challenges in TBF-based ML and offer insightful perspectives for future research regarding clustering, classification and information fusion. This survey not only fills a critical gap in the existing literature but also serves as a guiding beacon for future explorations, emphasizing the transformative role of TBF in advancing ML methodologies.http://www.sciencedirect.com/science/article/pii/S1319157823004585Theory of belief functionsUncertainty and imprecision reasoningMachine learningClusteringClassificationInformation fusion |
| spellingShingle | Zhe Liu Sukumar Letchmunan Representing uncertainty and imprecision in machine learning: A survey on belief functions Journal of King Saud University: Computer and Information Sciences Theory of belief functions Uncertainty and imprecision reasoning Machine learning Clustering Classification Information fusion |
| title | Representing uncertainty and imprecision in machine learning: A survey on belief functions |
| title_full | Representing uncertainty and imprecision in machine learning: A survey on belief functions |
| title_fullStr | Representing uncertainty and imprecision in machine learning: A survey on belief functions |
| title_full_unstemmed | Representing uncertainty and imprecision in machine learning: A survey on belief functions |
| title_short | Representing uncertainty and imprecision in machine learning: A survey on belief functions |
| title_sort | representing uncertainty and imprecision in machine learning a survey on belief functions |
| topic | Theory of belief functions Uncertainty and imprecision reasoning Machine learning Clustering Classification Information fusion |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823004585 |
| work_keys_str_mv | AT zheliu representinguncertaintyandimprecisioninmachinelearningasurveyonbelieffunctions AT sukumarletchmunan representinguncertaintyandimprecisioninmachinelearningasurveyonbelieffunctions |