Classification With Ant Colony Optimization

By David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens. IEEE Transactions on Evolutionary Computation, 2007

478 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/2fb67051362cb8209f7b9b248a4c7f97a2355052

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Citations in the corpus

Building comprehensible customer churn prediction models with advanced rule induction techniques (2011, methodology): “Since decision makers are reluctant to use unin-tuitive models, regardless of their accuracy, they will probably discard models that do not correspond with domain knowledge (Martens et al., 2007; Martens et al., 2006).”
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, methodology): “The ACO concept has previously been adopted to solve assignment problems [27] and scheduling problems [28] [29] with satisfactory results.”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, background): “In practice, often a limited number of highly predictive variables is preferred to be included, in order to improve the comprehensibility of classification models, even at the cost of a somewhat decreased discrimination power (Martens et al., 2007; Piramuthu, 2004).”
Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019, background): “However, they are time-consuming when solving complex problems (Martens et al. 2007).”
Building Acceptable Classification Models (2010, unknown): “, RIPPER [13], CN2 [12], and AntMiner+ [33].”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “The AntMiner+ algorithm uses ant-based induction that creates one rule at a time (Martens et al., 2007b).”
Flood hazard risk assessment model based on random forest (2015, background): “For example, SVMs are complex mathematical functions which are rather incomprehensible for human users (Martens et al., 2007).”
Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) (2012, methodology): “ACO has been used to solve many combinatorial optimization problems and other problems which have been demonstrated by Martens et al. (2007), Baucer, Bullnheimer, Hartl, and Strauss (2000), Stützle (1997) and Hu, Zhang, Xiao, and Li (2008). According to Venayagamoorthy and Harley (2007), ACO has an ”