Unsupervised Elimination of Redundant Features Using Genetic Programming

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

@InProceedings{DBLP:conf/ausai/NeshatianZ09,

• author = "Kourosh Neshatian and Mengjie Zhang",
• title = "Unsupervised Elimination of Redundant Features Using Genetic Programming",
• 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 = "432--442",
• 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_44",
• abstract = "While most feature selection algorithms focus on finding relevant features, few take the redundancy issue into account. We propose a nonlinear redundancy measure which uses genetic programming to find the redundancy quotient of a feature with respect to a subset of features. The proposed measure is unsupervised and works with unlabeled data. We introduce a forward selection algorithm which can be used along with the proposed measure to perform feature selection over the output of a feature ranking algorithm. The effectiveness of the proposed method is assessed by applying it to the output of the Chi-square feature ranker on a classification task. The results show significant improvements in the performance of decision tree and SVM classifiers.",

}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang

Citations