Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@InCollection{Zhang:2006:WSC,

• author = "Yang Zhang and Peter I. Rockett",
• title = "Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality",
• booktitle = "Soft Computing in Industrial Applications",
• publisher = "Springer",
• year = "2006",
• editor = "Ashraf Saad and Erel Avineri and Keshav Dahal and Muhammad Sarfraz and Rajkumar Roy",
• volume = "39",
• series = "Advances in Soft Computing",
• pages = "159--168",
• month = "18 " # sep # " - 6 " # oct,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cs.armstrong.edu/wsc11/pdf/pap107s2-file1.pdf",
• URL = "https://link.springer.com/chapter/10.1007/978-3-540-70706-6_15",
• DOI = "doi:10.1007/978-3-540-70706-6_15",
• abstract = "We present a multi-dimensional mapping strategy using multiobjective genetic programming (MOGP) to search for the (near-)optimal feature extraction pre-processing stages for pattern classification as well as optimizing the dimensionality of the decision space. We search for the set of mappings with optimal dimensionality to project the input space into a decision space with maximized class separability. The steady-state Pareto converging genetic programming (PCGP) has been used to implement this multi-dimensional MOGP. We examine the proposed method using eight benchmark datasets from the UCI database and the Statlog project to make quantitative comparison with conventional classifiers. We conclude that MMOGP outperforms the comparator classifiers due to its optimized feature extraction process.",
• notes = "WSC11 2006 published 2007",
• size = "10 pages",

}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations