Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/ausai/BhowanJZ11,

• author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
• title = "Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data",
• booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)",
• year = "2011",
• editor = "Dianhui Wang and Mark Reynolds",
• volume = "7106",
• series = "Lecture Notes in Computer Science",
• pages = "192--202",
• address = "Perth, Australia",
• month = dec # " 5-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-3-642-25832-9_20",
• size = "11 pages",
• abstract = "Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper develops a multi-objective genetic programming approach to evolving accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We explore why the ensembles can also be vulnerable to the learning bias using a range of unbalanced data sets. Based on the notion that smaller ensembles can be better than larger ensembles, we develop a new evolutionary-based pruning method to find groups of highly-cooperative individuals that can improve accuracy on the important minority class.",
• affiliation = "Evolutionary Computation Research Group, Victoria University of Wellington, New Zealand",
• bibdate = "2011-12-02",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#BhowanJZ11",

}

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang

Citations