GP ensembles for large-scale data classification

Gianluigi Folino

doi:10.1109/TEVC.2005.863627

GP ensembles for large-scale data classification

2006-10-01

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AI

Giandomenico Spezzano

DOI：10.1109/TEVC.2005.863627

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Abstract

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An extension of cellular genetic programming for data classification (CGPC) to induce an ensemble of predictors is presented. Two algorithms implementing the bagging and boosting techniques are described and compared with CGPC. The approach is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. The predictors are then combined to classify new tuples. Experiments on several data sets show that, by using a training set of reduced size, better classification accuracy can be obtained, but at a much lower computational cost.

Keywords:

bagging

boosting

classification

data mining

genetic programming (GP)

GP ensembles for large-scale data classification

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