Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Friedlander:2011:MaFRUGPfCVTW,

• title = "Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting",
• author = "Anna Friedlander and Kourosh Neshatian and Mengjie Zhang",
• pages = "940--947",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, GP, feature ranking algorithms, feature selection, feature weighting vector, learning classification, meta learning, online feature weighting method, probability, variable terminal weighting, feature extraction, learning (artificial intelligence), probability",
• DOI = "doi:10.1109/CEC.2011.5949719",
• abstract = "We propose an online feature weighting method for classification by genetic programming (GP). GP's implicit feature selection was used to construct a feature weighting vector, based on the fitness of solutions in which the features were found and the frequency at which they were found. The vector was used to perform feature ranking and to perform meta-learning by biasing terminal selection in mutation. The proposed meta-learning mechanism significantly improved the quality of solutions in terms of classification accuracy on an unseen test set. The probability of success---the probability of finding the desired solution within a given number of generations (fitness evaluations)---was also higher than canonical GP. The ranking obtained by using the GP-provided feature weighting was very highly correlated with the ranking obtained by commonly-used feature ranking algorithms. Population information during evolution can help shape search behaviour (meta-learning) and obtain useful information about the problem domain such as the importance of input features with respect to each other.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Anna Friedlander Kourosh Neshatian Mengjie Zhang

Citations