Article: Exception discovery using ant colony optimisation Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2018 Vol.4 No.1 pp.46 - 57 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-MinerPB) 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. Inderscience Publishers - linking academia, business and industry through research

Title: Exception discovery using ant colony optimisation

Authors: Saroj Ratnoo; Amarnath Pathak; Jyoti Ahuja; Jyoti Vashishtha

Addresses: Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, India ' Department of Computer Science and Engineering, National Institute of Technology, Mizoram, India ' Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, India ' Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, India

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.

Keywords: ant colony optimisation; ACO; classification; exception discovery; rule mining.

DOI: 10.1504/IJCSYSE.2018.090644

International Journal of Computational Systems Engineering, 2018 Vol.4 No.1, pp.46 - 57

Received: 09 Feb 2017
Accepted: 20 Aug 2017
Published online: 25 Mar 2018 *

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Title: Exception discovery using ant colony optimisation

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