Skip to main content

Research Repository

Advanced Search

Optimising rule-based classification in temporal data

Fattah, Polla; Aickelin, Uwe; Wagner, Christian

Authors

Polla Fattah

Uwe Aickelin



Abstract

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.

Citation

Fattah, P., Aickelin, U., & Wagner, C. (2016). Optimising rule-based classification in temporal data. Zanco Journal of Pure and Applied Sciences, 28(2),

Journal Article Type Article
Acceptance Date Jan 15, 2016
Publication Date May 26, 2016
Deposit Date Jun 20, 2016
Publicly Available Date Mar 29, 2024
Journal ZANCO Journal of Pure and Applied Sciences
Electronic ISSN 2412-3986
Peer Reviewed Peer Reviewed
Volume 28
Issue 2
Keywords temporal classification; temporal data; public goods game; optimisation; rule-based classification
Public URL https://nottingham-repository.worktribe.com/output/788447
Publisher URL http://zancojournals.su.edu.krd/index.php/JPAS/article/view/561