Exception discovery using ant colony optimisation
by Saroj Ratnoo; Amarnath Pathak; Jyoti Ahuja; Jyoti Vashishtha
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 4, No. 1, 2018

Abstract: Ant colony optimisation (ACO) algorithms have been used to discover accurate, and comprehensible classification rules. Discovering exceptions using ACO is an underexplored area of research. Most of the classification algorithms focus on discovering rules with high generality. Since exceptions have low support, these often get ignored as noise. This paper proposes an ant colony optimisation (ACO)-based algorithm to discover classification rules in if-then-unless framework, where the unless part contains exceptions. We have conducted experiments on ten datasets from the UCI machine learning repository. The suggested algorithm is found to be competitive with the two well-known ACO-based classification algorithms (Ant-Miner and cAnt-Miner_PB) with respect to predictive accuracy and comprehensibility. The algorithm has been able to capture a number of exceptions across several datasets. The classification rules discovered with exceptions are accurate, semantically comprehensible and interesting. These rules provide an opportunity to amend one's decision in exceptional circumstances.

Online publication date: Sun, 25-Mar-2018

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