Classification as Clustering: A Pareto Cooperative-Competitive GP Approach

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

@Article{McIntyre:2011:EC,

• author = "Andrew R. McIntyre and Malcolm I. Heywood",
• title = "Classification as Clustering: A Pareto Cooperative-Competitive GP Approach",
• journal = "Evolutionary Computation",
• year = "2011",
• volume = "19",
• number = "1",
• pages = "137--166",
• month = "Spring",
• keywords = "genetic algorithms, genetic programming, MOGA, Pareto",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/EVCO_a_00016",
• size = "30 pages",
• abstract = "Intuitively population based algorithms such as Genetic Programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to pre-specifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parametrisation of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member represent an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from Evolutionary Multi-objective Optimisation (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet non-overlaping behaviours; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced data sets. Benchmarking is performed against recent examples of non-linear SVM classifiers over twelve UCI data sets with between 150 and 200,000 training instances. Solutions from the proposed Coevolutionary Multi-objective GP framework appear to provide a good balance between classification performance and model complexity, especially as the data set instance count increases.",

}

Genetic Programming entries for Andrew R McIntyre Malcolm Heywood

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