A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Neshatian:2012:ieeeTEC,

• author = "Kourosh Neshatian and Mengjie Zhang and Peter Andreae",
• title = "A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2012",
• volume = "16",
• number = "5",
• pages = "645--661",
• month = oct,
• keywords = "genetic algorithms, genetic programming, classification, decision trees, feature construction, rule-based systems",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2011.2166158",
• size = "17 pages",
• abstract = "Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance. Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically. This article proposes GPMFC, a multiple feature construction system for classification problems using genetic programming (GP). The article takes a non-wrapper approach by introducing a filter-based measure of goodness for constructed features. The constructed, high-level features are functions of original input features. These functions are evolved by GP using an entropy-based fitness function that maximises the purity of class intervals. A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem. The constructed features are used to transform the original input space to a new space with better separability. Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers. The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers. The constructed features help improve the learning performance of symbolic learners. The constructed features, however, may lack intelligibility.",
• notes = "Also known as \cite{6151112}",

}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang Peter Andreae

Citations