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Journal Articles Genetic Programming and Evolvable Machines Year : 2016

Prediction of Expected Performance for a Genetic Programming Classifier


The study of problem difficulty is an open issue in Genetic Programming (GP). The goal of this work is to generate models that predict the expected performance of a GPbased classifier when it is applied to an unseen task. Classification problems are described using domain-specific features, some of which are proposed in this work, and these features are given as input to the predictive models. These models are referred to as predictors of expected performance (PEPs). We extend this approach by using an ensemble of specialized predictors (SPEP), dividing classification problems into specified groups and choosing the corresponding SPEP. The proposed predictors are trained using 2D synthetic classification problems with balanced datasets. The models are then used to predict the performance of the GP classifier on unseen realworld datasets that are multidimensional and imbalanced. Moreover, as we know, this work is the first to provide a performance prediction of the GP classifier on test data, while previous works focused on predicting training performance. Accurate predictive models are generated by posing a symbolic regression task and solving it with GP. These results are achieved by using highly descriptive features and including a dimensionality reduction stage that simplifies the learning and testing process. The proposed approach could be extended to other classification algorithms and used as the basis of an expert system for algorithm selection.

Dates and versions

hal-01252141 , version 1 (07-01-2016)



Yuliana Martinez, Leonardo Trujillo, Pierrick Legrand, Edgar Galvan-Lopez. Prediction of Expected Performance for a Genetic Programming Classifier. Genetic Programming and Evolvable Machines, 2016, 17 (4), pp.409-449. ⟨10.1007/s10710-016-9265-9⟩. ⟨hal-01252141⟩


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