From interval-valued data to general type-2 fuzzy sets
In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated...
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| Format: | Article |
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IEEE
2014
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| Online Access: | https://eprints.nottingham.ac.uk/29176/ |
| _version_ | 1848793729826029568 |
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| author | Wagner, Christian Miller, Simon Garibaldi, Jonathan M. Anderson, Derek T. Havens, Timothy C. |
| author_facet | Wagner, Christian Miller, Simon Garibaldi, Jonathan M. Anderson, Derek T. Havens, Timothy C. |
| author_sort | Wagner, Christian |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated surveys can be modeled using type-1, interval type-2, or general type-2 FSs based on zSlices. The proposed approach is designed to minimize any loss of information when transferring the interval-based data into FS models, and to avoid, as much as possible, assumptions about the distribution of the data. Furthermore, our approach does not rely on data preprocessing or outlier removal, which can lead to the elimination of important information. Different types of uncertainty contained within the data, namely intra- and inter-source uncertainty, are identified and modeled using the different degrees of freedom of type-2 FSs, thus providing a clear representation and separation of these individual types of uncertainty present in the data. We provide full details of the proposed approach, as well as a series of detailed examples based on both real-world and synthetic data. We perform comparisons with analogue techniques to derive FSs from intervals, namely the interval approach and the enhanced interval approach, and highlight the practical applicability of the proposed approach. |
| first_indexed | 2025-11-14T19:04:56Z |
| format | Article |
| id | nottingham-29176 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:04:56Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-291762020-05-04T16:45:05Z https://eprints.nottingham.ac.uk/29176/ From interval-valued data to general type-2 fuzzy sets Wagner, Christian Miller, Simon Garibaldi, Jonathan M. Anderson, Derek T. Havens, Timothy C. In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated surveys can be modeled using type-1, interval type-2, or general type-2 FSs based on zSlices. The proposed approach is designed to minimize any loss of information when transferring the interval-based data into FS models, and to avoid, as much as possible, assumptions about the distribution of the data. Furthermore, our approach does not rely on data preprocessing or outlier removal, which can lead to the elimination of important information. Different types of uncertainty contained within the data, namely intra- and inter-source uncertainty, are identified and modeled using the different degrees of freedom of type-2 FSs, thus providing a clear representation and separation of these individual types of uncertainty present in the data. We provide full details of the proposed approach, as well as a series of detailed examples based on both real-world and synthetic data. We perform comparisons with analogue techniques to derive FSs from intervals, namely the interval approach and the enhanced interval approach, and highlight the practical applicability of the proposed approach. IEEE 2014-03-11 Article PeerReviewed Wagner, Christian, Miller, Simon, Garibaldi, Jonathan M., Anderson, Derek T. and Havens, Timothy C. (2014) From interval-valued data to general type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 23 (2). pp. 248-269. ISSN 1063-6706 Survey data zSlices Uncertainty Computing With Words Type-2 Agreement Interval Agreement Approach http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6762925&filter%3DAND%28p_IS_Number%3A7070767%29 doi:10.1109/TFUZZ.2014.2310734 doi:10.1109/TFUZZ.2014.2310734 |
| spellingShingle | Survey data zSlices Uncertainty Computing With Words Type-2 Agreement Interval Agreement Approach Wagner, Christian Miller, Simon Garibaldi, Jonathan M. Anderson, Derek T. Havens, Timothy C. From interval-valued data to general type-2 fuzzy sets |
| title | From interval-valued data to general type-2 fuzzy sets |
| title_full | From interval-valued data to general type-2 fuzzy sets |
| title_fullStr | From interval-valued data to general type-2 fuzzy sets |
| title_full_unstemmed | From interval-valued data to general type-2 fuzzy sets |
| title_short | From interval-valued data to general type-2 fuzzy sets |
| title_sort | from interval-valued data to general type-2 fuzzy sets |
| topic | Survey data zSlices Uncertainty Computing With Words Type-2 Agreement Interval Agreement Approach |
| url | https://eprints.nottingham.ac.uk/29176/ https://eprints.nottingham.ac.uk/29176/ https://eprints.nottingham.ac.uk/29176/ |