Feature Subset Selection by Bayesian network-based optimization Export

by: Inaki Inza, Pedro Larranaga, R. Etxeberria, Basilio Sierra

Artificial Intelligence, Vol. 123, No. 1-2. (2000), pp. 157-184.

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Abstract

A new method for Feature Subset Selection in Machine Learning, FSS-EBNA (Feature Subset Selection by Estimation of Bayesian Network Algorithm), is presented. FSS-EBNA is an evolutionary, population-based, randomized search algorithm, and it can be executed when domain knowledge is not available. A wrapper approach, over Naive-Bayes and ID3 learning algorithms, is used to evaluate the goodness of each visited solution. FSS-EBNA, based on the EDA (Estimation of Distribution Algorithm) paradigm,...

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TY - JOUR ID - citeulike:1319147 L3 - citeulike-article-id:1319147 N2 - A new method for Feature Subset Selection in Machine Learning, FSS-EBNA (Feature Subset Selection by Estimation of Bayesian Network Algorithm), is presented. FSS-EBNA is an evolutionary, population-based, randomized search algorithm, and it can be executed when domain knowledge is not available. A wrapper approach, over Naive-Bayes and ID3 learning algorithms, is used to evaluate the goodness of each visited solution. FSS-EBNA, based on the EDA (Estimation of Distribution Algorithm) paradigm,... IS - 1-2 JF - Artificial Intelligence EP - 184 TI - Feature Subset Selection by Bayesian network-based optimization VL - 123 SP - 157 KW - bayesian AU - Inza, Inaki AU - Larranaga, Pedro AU - Etxeberria, R AU - Sierra, Basilio PY - 2000/// UR - http://citeseer.ist.psu.edu/inza99feature.html ER -

Feature Subset Selection by Bayesian network-based optimization Export

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