Two Steps Genetic Programming for Big Data - Perspective of Distributed and High-Dimensional Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Huang:2015:ieeeBigData,

• author = "Jih-Jeng Huang",
• booktitle = "2015 IEEE International Congress on Big Data",
• title = "Two Steps Genetic Programming for Big Data - Perspective of Distributed and High-Dimensional Data",
• year = "2015",
• pages = "753--756",
• abstract = "The term big data has been the most popular topic in recent years in practice, academe and government for realizing the value of data. Then, many information technologies and software are proposed to deal with big data, such as Hadoop, NoSQL databases, and cloud computing. However, these tools can only help us to store, manage, search, and control data rather than extract knowledge from big data. The only way to mine the nugget from big data is to have the ability to analyse them. The characteristics of complexity of big data, e.g., Volume and variety make traditional data mining algorithms invalid. In this paper, we deal with big data by solving distributed and high-dimensional problems. We propose a novel algorithm to effectively extract knowledge from big data. According to the empirical study, the propose method can handle big data soundly.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/BigDataCongress.2015.125",
• ISSN = "2379-7703",
• month = jun,
• notes = "Also known as \cite{7207309}",

}

Genetic Programming entries for Jih-Jeng Huang

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