Binary Classification Using Genetic Programming: Evolving Discriminant Functions with Dynamic Thresholds

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

@InProceedings{conf/pakdd/JongN13,

• author = "Jill {de Jong} and Kourosh Neshatian",
• title = "Binary Classification Using Genetic Programming: Evolving Discriminant Functions with Dynamic Thresholds",
• booktitle = "Trends and Applications in Knowledge Discovery and Data Mining",
• editor = "Jiuyong Li and Longbing Cao and Can Wang and Kay Chen Tan and Bo Liu and Jian Pei and Vincent S. Tseng",
• year = "2013",
• volume = "7867",
• series = "Lecture Notes in Computer Science",
• pages = "464--474",
• address = "Gold Coast, Australia",
• month = apr # " 14-17",
• publisher = "Springer",
• note = "Revised Selected Papers",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-08-27",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/pakdd/pakdd2013-w.html#JongN13",
• isbn13 = "978-3-642-40318-7",
• URL = "http://dx.doi.org/10.1007/978-3-642-40319-4",
• DOI = "doi:10.1007/978-3-642-40319-4_40",
• size = "11 pages",
• abstract = "Binary classification is the problem of predicting which of two classes an input vector belongs to. This problem can be solved by using genetic programming to evolve discriminant functions which have a threshold output value that distinguishes between the two classes. The standard approach is to have a static threshold value of zero that is fixed throughout the evolution process. Items with a positive function output value are identified as one class and items with a negative function output value as the other class. We investigate a different approach where an optimum threshold is dynamically determined for each candidate function during the fitness evaluation. The optimum threshold is the one that achieves the lowest misclassification cost. It has an associated class translation rule for output values either side of the threshold value. The two approaches have been compared experimentally using four different datasets. Results suggest the dynamic threshold approach consistently achieves higher performance levels than the standard approach after equal numbers of fitness calls.",
• notes = "PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, 2013",

}

Genetic Programming entries for Jill de Jong Kourosh Neshatian

Citations