Optimising Rule-Based Classification in Temporal Data

Polla Fattah, Uwe Aickelin, Christian Wagner


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.


temporal classification; temporal data; public goods game; optimisation; rule-based classification

Full Text:


Copyright (c) 2017 ZANCO Journal of Pure and Applied Sciences

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

ZANCO Journal of Pure and Applied Sciences (print version: ISSN 2218-0230 online version: ISSN 2412-3986, DOI: 10.21271) is published by Salahaddin University-Erbil / Department of Scientific Publications. Responsibility for the contents rests upon the authors and not upon  Salahaddin University-Erbil or the Journal Editorial and Advisory Boards. 

Department of Scientific Publication Office: The Central Library of Salahaddin University-Erbil, Kirkuk Road, Erbil, Kurdistan, Iraq. Cell Phone: +964 (0)750 7761675, email: zanco.scientific@su.edu.krd. www.su.edu.krd, www.zancojournals.su.edu.krd

Copyright and Reprint Permission: It is the policy of ZANCO to own the copyright to the technical contributions it publishes and to facilitate the appropriate reuse of this material by others. Photocopying is permitted with credit to the source for individuals for individual use.

Copyright © 2017. All Rights Reserved. Salahaddin University-Erbil