A survey of evolutionary algorithms for data mining and knowledge discovery

By Alex Alves Freitas. 2003

503 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8a1d2761ce0e1e7bdc0035926f57754f3d1eda3c

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Citations in the corpus

A Genetic Algorithm-Based Approach for Building Accurate Decision Trees (2003, methodology): “GP and GAs have been used in a variety of data mining applications like clustering and rule discovery (see Freitas 2002a, b) and there are tradeoffs to consider when choosing between the methods.”
Accurate and interpretable regression trees using oracle coaching (2014, unknown): “[5] A.”
Predicting student performance: an application of data mining methods with an educational Web-based system (2003, methodology): “Genetic Algorithms have been shown to be an effective tool to use in data mining and pattern recognition [5]-[12].”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, methodology): “In traditional data mining tasks, evolutionary algorithms can easily be used for both classification and clustering as well as for data preparation in the form of attribute generation and selection [27].”
Particle Swarm based Data Mining Algorithms for classification tasks (2004, methodology): “In rule classifier systems there are two distinct approaches to individual or particle representation: the Michigan and the Pittsburgh approaches [11].”
A survey on optimization metaheuristics (2013, methodology): “EAs have been widely applied with a good measure of success to combinatori al optimization problems [33,26], constrained optimization problems [55], Data Mining and Knowledge Discovery [97], etc.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “[92] A.A. Freitas, A survey of evolutionary algorithms for data mining and knowledge discovery, in: Advances in Evolutionary Computing, SpringerVerlag, New York, USA, 2003.”
Profit Driven Decision Trees for Churn Prediction (2017, background): “Although ProfTree has a similar complexity as the approach by Grubinger et al. (2014), we acknowledge that the training times of the evolutionary algorithm (EA) are rather slow compared to other classification algorithms.”