Optimising rule-based classification in temporal data
This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to cla...
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| Format: | Article |
| Language: | English |
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Salahaddin University-Erbil
2016
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| Online Access: | https://eprints.nottingham.ac.uk/34220/ |
| _version_ | 1848794801258889216 |
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| author | Fattah, Polla Aickelin, Uwe Wagner, Christian |
| author_facet | Fattah, Polla Aickelin, Uwe Wagner, Christian |
| author_sort | Fattah, Polla |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies’ share price stability over a period of time or how to classify students’ preferences for subjects while they are progressing through school. A specific case the paper considers is the strategy of players in public goods games (as common in economics) across multiple consecutive games. Initial classification starts from expert definitions specifying class allocation for players based on aggregated attributes of the temporal data. Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data. The compactness of the classes is measured by a cost function based on internal cluster indices like the Dunn Index, distance measures like Euclidean distance or statistically derived measures like standard deviation. The paper discusses the approach in the context of incorporating changing player strategies in the aforementioned public good games, where common classification approaches so far do not consider such changes in behaviour resulting from learning or in-game experience. By using the proposed process for classifying temporal data and the actual players’ contribution during the games, we aim to produce a more refined classification which in turn may inform the interpretation of public goods game data. |
| first_indexed | 2025-11-14T19:21:58Z |
| format | Article |
| id | nottingham-34220 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:21:58Z |
| publishDate | 2016 |
| publisher | Salahaddin University-Erbil |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-342202018-12-03T16:11:27Z https://eprints.nottingham.ac.uk/34220/ Optimising rule-based classification in temporal data Fattah, Polla Aickelin, Uwe Wagner, Christian This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies’ share price stability over a period of time or how to classify students’ preferences for subjects while they are progressing through school. A specific case the paper considers is the strategy of players in public goods games (as common in economics) across multiple consecutive games. Initial classification starts from expert definitions specifying class allocation for players based on aggregated attributes of the temporal data. Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data. The compactness of the classes is measured by a cost function based on internal cluster indices like the Dunn Index, distance measures like Euclidean distance or statistically derived measures like standard deviation. The paper discusses the approach in the context of incorporating changing player strategies in the aforementioned public good games, where common classification approaches so far do not consider such changes in behaviour resulting from learning or in-game experience. By using the proposed process for classifying temporal data and the actual players’ contribution during the games, we aim to produce a more refined classification which in turn may inform the interpretation of public goods game data. Salahaddin University-Erbil 2016-05-26 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/34220/1/561 Fattah, Polla, Aickelin, Uwe and Wagner, Christian (2016) Optimising rule-based classification in temporal data. ZANCO Journal of Pure and Applied Sciences, 28 (2). pp. 135-146. ISSN 2412-3986 temporal classification; temporal data; public goods game; optimisation; rule-based classification http://zancojournals.su.edu.krd/index.php/JPAS/article/view/561 15 15 |
| spellingShingle | temporal classification; temporal data; public goods game; optimisation; rule-based classification Fattah, Polla Aickelin, Uwe Wagner, Christian Optimising rule-based classification in temporal data |
| title | Optimising rule-based classification in temporal data |
| title_full | Optimising rule-based classification in temporal data |
| title_fullStr | Optimising rule-based classification in temporal data |
| title_full_unstemmed | Optimising rule-based classification in temporal data |
| title_short | Optimising rule-based classification in temporal data |
| title_sort | optimising rule-based classification in temporal data |
| topic | temporal classification; temporal data; public goods game; optimisation; rule-based classification |
| url | https://eprints.nottingham.ac.uk/34220/ https://eprints.nottingham.ac.uk/34220/ https://eprints.nottingham.ac.uk/34220/ |