Multi-view semi-supervised learning using genetic programming interpretable classification rules

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{garcia-martinez:2017:CEC,

• author = "Carlos Garcia-Martinez and Sebastian Ventura",
• booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Multi-view semi-supervised learning using genetic programming interpretable classification rules",
• year = "2017",
• editor = "Jose A. Lozano",
• pages = "573--579",
• address = "Donostia, San Sebastian, Spain",
• publisher = "IEEE",
• isbn13 = "978-1-5090-4601-0",
• abstract = "Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However to our knowledge, the multi-view learning paradigm has not been applied to produce interpretable rule-based classifiers before. In this work, we present a multi-view extension of a grammar-based genetic programming model for inducing rules for semi-supervised contexts. Its idea is to evolve several populations, and their corresponding views, favouring both the accuracy of the predictions for the labelled patterns and the prediction agreement with the other views for unlabelled ones. We have carried out experiments with two to five views, on six common datasets for fully-supervised learning that have been partially anonymised for our semi-supervised study. Our results show that the multi-view paradigm allows to obtain slightly better rule-based classifiers, and that two views becomes preferred.",
• keywords = "genetic algorithms, genetic programming, learning (artificial intelligence), pattern classification, grammar-based genetic programming model, interpretable classification rules, multiview semi-supervised learning, rule-based classifiers, semi-supervised contexts, Context, Kernel, Semisupervised learning, Sociology, Statistics, Training",
• isbn13 = "978-1-5090-4601-0",
• DOI = "doi:10.1109/CEC.2017.7969362",
• month = "5-8 " # jun,
• notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969362}",

}

Genetic Programming entries for Carlos Garcia-Martinez Sebastian Ventura

Citations