Lazy Learning for Multi-class Classification Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8229

@InProceedings{conf/icic/JabeenB11,

• author = "Hajira Jabeen and Abdul Rauf Baig",
• title = "Lazy Learning for Multi-class Classification Using Genetic Programming",
• booktitle = "7th International Conference on Advanced Intelligent Computing Theories and Applications, with Aspects of Artificial Intelligence (ICIC 2011)",
• year = "2011",
• editor = "De-Shuang Huang and Yong Gan and Phalguni Gupta and M. Michael Gromiha",
• volume = "6839",
• series = "Lecture Notes in Computer Science",
• pages = "177--182",
• address = "Zhengzhou, China",
• month = aug # " 11-14",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-25943-2",
• DOI = "doi:10.1007/978-3-642-25944-9_23",
• size = "6 pages",
• abstract = "In this paper we have proposed a lazy learning mechanism for multiclass classification using genetic programming. This method is an improvement of traditional binary decomposition method for multiclass classification. We train classifiers for individual classes for a certain number of generations. Individual trained classifiers for each class are combined in a single chromosome. A population of such chromosomes is created and evolved further. This method suppresses the conflicting situations common in binary decomposition method. The proposed lazy learning method has performed better than traditional binary decomposition method over five benchmark datasets taken from UCI ML repository.",
• notes = "Revised Selected Papers. Published 2012",
• affiliation = "Iqra University, 5 H-9/1, Islamabad, Pakistan",
• bibdate = "2012-01-05",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2011-2.html#JabeenB11",

}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

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