Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt

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@Article{Garcia-Martinez:2020:IJCIS,

• author = "Carlos Garcia-Martinez and Sebastian Ventura",
• title = "Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt",
• year = "2020",
• journal = "International Journal of Computational Intelligence Systems",
• volume = "13",
• number = "1",
• pages = "576--590",
• keywords = "genetic algorithms, genetic programming, Multi-view learning, Rule-based classification, Comprehensibility, Semi-supervised learning, Co-training, Grammar-based genetic programming",
• publisher = "Atlantis Press SARL",
• ISSN = "1875-6883",
• URL = "https://doi.org/10.2991/ijcis.d.200511.002",
• DOI = "doi:10.2991/ijcis.d.200511.002",
• abstract = "Multi-view learning analyzes the information from several perspectives and has largely been applied on semi-supervised contexts. It has not been extensively analyzed for inducing interpretable rule-based classifiers. We present a multi-view and grammar-based genetic programming model for inducing rules for semi-supervised contexts. It evolves several populations and views, and promotes both accuracy and agreement among the views. This work details how and why common practices may not produce the expected results when inducing rule-based classifiers under this methodology.",

}

Genetic Programming entries for Carlos Garcia-Martinez Sebastian Ventura

Citations