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WIREs Data Mining Knowl Discov
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Large‐scale data mining using genetics‐based machine learning

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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.

Figure 1.

Rule generated from a Bioinformatics dataset with 300 attributes.

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Figure 2.

Pseudocode of the match algorithm for an hyperrectangle rule representation.

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Figure 3.

Example of a rule in the attribute list knowledge representation with four expressed attributes: 1, 3, 4, and 7. ln = lower bound of attribute n, un= upper bound of attribute n, c1 = Class 1 of the domain. © Memetic Computing by Springer‐Verlag Berlin/Heidelberg. Reproduced with permission of Springer‐Verlag Berlin/Heidelberg in the format Journal via Copyright Clearance Center.

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Figure 4.

Parallel match algorithm using binary encoding (top) and Streaming SIMD Extensions (SSE) vectorial operations (bottom). Reprinted with permission from Ref 85. Copyright 2006 Association for Computing Machinery, Inc.

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Figure 5.

Diagram of the computer unified device architecture based fitness computation pipeline of the BioHEL genetics‐based machine learning system.

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Figure 6.

Parallel architecture of the NAX system. © Natural Computing by Kluwer Academic Publishers. Reproduced with permission of Kluwer Academic Publishers in the format Journal via Copyright Clearance Center.

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Figure 7.

Visualization of a large number of rule sets as a network of interactions.104 Nodes = attributes. Edges = attributes appearing together in a rule. © The Plant Cell by American Society of Plant Physiologists Copyright 2013 Reproduced with permission of American Society of Plant Biologists in the format Republish in a journal via Copyright Clearance Center.

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Figure 8.

Representation of the training and prediction process for contact map prediction using the BioHEL genetics‐based machine learning system.

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