Using optimisation techniques to granulise rough set partitions
Date
2009-01-26T08:58:40Z
Authors
Crossingham, Bodie
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Abstract
Rough set theory (RST) is concerned with the formal approximation of crisp sets
and is a mathematical tool which deals with vagueness and uncertainty. RST can be
integrated into machine learning and can be used to forecast predictions as well as to
determine the causal interpretations for a particular data set. The work performed
in this research is concerned with using various optimisation techniques to granulise
the rough set input partitions in order to achieve the highest forecasting accuracy
produced by the rough set. The forecasting accuracy is measured by using the area
under the curve (AUC) of the receiver operating characteristic (ROC) curve. The
four optimisation techniques used are genetic algorithm, particle swarm optimisation,
hill climbing and simulated annealing. This newly proposed method is tested
on two data sets, namely, the human immunodeficiency virus (HIV) data set and
the militarised interstate dispute (MID) data set. The results obtained from this
granulisation method are compared to two previous static granulisation methods,
namely, equal-width-bin and equal-frequency-bin partitioning. The results conclude
that all of the proposed optimised methods produce higher forecasting accuracies
than that of the two static methods. In the case of the HIV data set, the hill climbing
approach produced the highest accuracy, an accuracy of 69.02% is achieved in a
time of 12624 minutes. For the MID data, the genetic algorithm approach produced
the highest accuracy. The accuracy achieved is 95.82% in a time of 420 minutes.
The rules generated from the rough set are linguistic and easy-to-interpret, but this
does come at the expense of the accuracy lost in the discretisation process where
the granularity of the variables are decreased.
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Keywords
Bioinformatics application, HIV modelling, Evolutionary optimisation techniques, Rough set theory