Evolutionary Feature Manipulation in Data Mining/Big Data

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

@Article{Xue:2017:sigevo,

• author = "Bing Xue and Mengjie Zhang",
• title = "Evolutionary Feature Manipulation in Data Mining/Big Data",
• journal = "SIGEVOlution",
• year = "2017",
• volume = "10",
• number = "1",
• pages = "4--11",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.sigevolution.org/issues/SIGEVOlution1001.pdf",
• DOI = "doi:10.1145/3089251.3089252",
• size = "8 pages",
• abstract = "Known as the GIGO (Garbage In, Garbage Out) principle, the quality of the input data highly influences or even determines the quality of the output of any machine learning, big data and data mining algorithm. The input data which is often represented by a set of features may suffer from many issues. Feature manipulation is an effective means to improve the feature set quality, but it is a challenging task. Evolutionary computation (EC) techniques have shown advantages and achieved good performance in feature manipulation. This paper reviews recent advances on EC based feature manipulation methods in classification, clustering, regression, incomplete data, and image analysis, to provide the community the state-of-the-art work in the field.",

}

Genetic Programming entries for Bing Xue Mengjie Zhang

Citations