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 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.
