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Large‐scale data mining using genetics‐based machine learning
Author(s) -
Bacardit Jaume,
Llorà Xavier
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1078
Subject(s) - exploit , computer science , scalability , machine learning , artificial intelligence , big data , variety (cybernetics) , scale (ratio) , data science , parallelism (grammar) , data mining , database , parallel computing , physics , computer security , quantum mechanics
In the last decade, genetics‐based machine learning methods have shown their competence in large‐scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data‐intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics‐based machine learning methods. © 2013 Wiley Periodicals, Inc. This article is categorized under: Technologies > Classification Technologies > Computational Intelligence