Classification With Ant Colony Optimization

Martens, David; Backer, Manu De; Haesen, Raf; Vanthienen, Jan; Snoeck, Monique; Baesens, Bart

doi:10.1109/tevc.2006.890229

Cited by 371 publications

(186 citation statements)

References 22 publications

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“…David Martens et al [41], proposed a Max-Min Ant System based algorithm known as Ant-Miner+. The new classification rule mining approach is based on the bio inspired Ant-Miner.…”

Section: Antminer+mentioning

confidence: 99%

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Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Ali¹,

Shahzad²

2017

ijacsa

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“…David Martens et al [41], proposed a Max-Min Ant System based algorithm known as Ant-Miner+. The new classification rule mining approach is based on the bio inspired Ant-Miner.…”

Section: Antminer+mentioning

confidence: 99%

“…In Ant-Miner3 new pheromone update mechanism is proposed which resulted promising results. The Ant-Miner+ [41], variant of Ant-Miner introduced new relations for initial pheromone value, rule quality evaluation and pheromone value update. New pheromone value is boosted directly to the quality of the rule.…”

Section: Aco-minermentioning

confidence: 99%

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Ali¹,

Shahzad²

2017

ijacsa

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“…The Confidence + Coverage was used in AntMiner+ algorithm [9]. The confidence measures the fraction of examples covered by the rule correctly, and the coverage measures the importance of the rule by calculating the fraction of correctly covered examples against all remaining examples.…”

Section: S the Total Number Of Examples (Tp + Fp + Tn + Fn)mentioning

confidence: 99%

A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm

Medland

Otero

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACObased procedure to search the best rule. Recently, an improved strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules instead of individual rules. In the cAntMinerPB algorithm, the rule quality function has a smaller role and the search is guided by the quality of a list of rules. This paper sets out to determine the effect of different rule and list quality functions in terms of both predictive accuracy and size of the discovered model in cAnt-MinerPB. The comparative analysis is performed using 12 data sets from the UCI Machine Learning repository and shows that the effect of the rule quality functions in cAnt-MinerPB is different from the results previously presented in the literature.

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“…Another popular alternative is to use classification trees, with its origins both in statistics ( Breiman 1984) and machine learning (Quinlan 1993), though of course this ends up not with a scorecard but with groups of customers described by combinations of their characteristics where each group is classified as either Good or Bad . However any classification approach can be applied to the credit scoring problem and so in the past twenty years researchers have tried neural nets ( Desai et al 1997, Malhotra andMalhotra 2002), support vector machines ( Huang et al 2007, van Gestel et al 2003, Bellotti and Crook 2009a , genetic algorithms ( Desai et al 1997,Ong et al 2005, nearest neighbour methods (Chatterjee and Barcun (1970), Henley and Hand (1996)) and ant colony optimization ( Martens et al 2007). So what methodology gives a scorecard with the best discrimination in credit scoring?…”

Section: Challenge 1: Finding the "Silver Bullet" Or Is There A Bettementioning

confidence: 99%

Consumer finance: challenges for operational research

Thomas

2010

Journal of the Operational Research Society

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: Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance-and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance.

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Classification With Ant Colony Optimization

Cited by 371 publications

References 22 publications

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm

Consumer finance: challenges for operational research

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