Multi-Objective Genetic Programming for Classification with Unbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{DBLP:conf/ausai/BhowanZJ09,

• author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
• title = "Multi-Objective Genetic Programming for Classification with Unbalanced Data",
• booktitle = "Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)",
• year = "2009",
• editor = "Ann E. Nicholson and Xiaodong Li",
• volume = "5866",
• series = "Lecture Notes in Computer Science",
• pages = "370--380",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• address = "Melbourne, Australia",
• month = dec # " 1-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-10438-1",
• DOI = "doi:10.1007/978-3-642-10439-8_38",
• abstract = "Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.",

}

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston

Citations