A Survey on the Application of Genetic Programming to Classification

By Pedro G. Espejo, Sebastián Ventura, Francisco Herrera. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2010

590 citations recorded by the source corpus.

Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/6784d2ba45b732626eac83e64ebe6c8779aaef50

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Citations in the corpus

Genetic programming for feature construction and selection in classification on high-dimensional data (2015, methodology): “Although application of GP for inducing classifiers usually implies a feature selection and construction process [10], this section is restricted to studies that use GP explicitly for preprocessing purposes.”
Machine Learning in Financial Crisis Prediction: A Survey (2012, background): “This corresponds to important survey literature, depicting the popularity of these techniques in many domain problems, [17], [38], [126], [155], [156], [169].”
Automatic Modulation Classification Using Combination of Genetic Programming and KNN (2012, methodology): “In [33] Espejo, Ventura and Herrera gave a survey on the application of genetic programming for classification purpose.”
Educational Data Mining: A Review of the State of the Art (2010, background): “acteristics inherent in the items and based on a training set of previously labeled items [75].”
Web usage mining for predicting final marks of students that use Moodle courses (2013, methodology): “Decisiontreeshavebeenemployedsuccessfullyindif-ferentdomainslikeagriculture[38],medicine[39],networking[40],etc. [43],theSupervisedInductiveAlgorithm(SIA)[44],ageneticalgorithmusingreal-valuedgenes(Corcoran)[45]and a Grammar-based genetic programming algorithm (GGP) [46].”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “[27] state that the large number of parameters that must be defined in order to have a working”