Multi-Objective Genetic Programming for Visual Analytics

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

@InProceedings{icke:2011:EuroGP,

• author = "Ilknur Icke and Andrew Rosenberg",
• title = "Multi-Objective Genetic Programming for Visual Analytics",
• booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011",
• year = "2011",
• month = "27-29 " # apr,
• editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado",
• series = "LNCS",
• volume = "6621",
• publisher = "Springer Verlag",
• address = "Turin, Italy",
• pages = "322--334",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming: poster",
• DOI = "doi:10.1007/978-3-642-20407-4_28",
• abstract = "Visual analytics is a human-machine collaboration to data modelling where extraction of the most informative features plays an important role. Although feature extraction is a multi-objective task, the traditional algorithms either only consider one objective or aggregate the objectives into one scalar criterion to optimise. In this paper, we propose a Pareto-based multi-objective approach to feature extraction for visual analytics applied to data classification problems. We identify classifiability, visual interpretability and semantic interpretability as the three equally important objectives for feature extraction in classification problems and define various measures to quantify these objectives. Our results on a number of benchmark datasets show consistent improvement compared to three standard dimensionality reduction techniques. We also argue that exploration of the multiple Pareto-optimal models provide more insight about the classification problem as opposed to a single optimal solution.",
• notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011",

}

Genetic Programming entries for Ilknur Icke Andrew Rosenberg

Citations