tutorial

Large scale data mining using genetics-based machine learning

Authors:

Jaume Bacardit,

Xavier LloràAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 3381 - 3412

https://doi.org/10.1145/1570256.1570424

Published: 08 July 2009 Publication History

Abstract

We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.

This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for data mining methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms, hardware solutions, parallel models and data-intensive computing. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.

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Cited By

Fan MXiao KSun LZhang SXu Y(2022)Automated Hyperparameter Optimization of Gradient Boosting Decision Tree Approach for Gold Mineral Prospectivity Mapping in the Xiong’ershan AreaMinerals10.3390/min1212162112:12(1621)Online publication date: 16-Dec-2022
https://doi.org/10.3390/min12121621
Sood ISharma V(2020)A comparative study of Distributed Large Scale Data Mining AlgorithmsBSSS Journal of Computer10.51767/jc1102Online publication date: 25-May-2020
https://doi.org/10.51767/jc1102
Lahmer HOueslati ALachiri Z(2020)Classification of DNA Microarrays Using Deep Learning to identify Cell Cycle Regulated Genes2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)10.1109/ATSIP49331.2020.9231888(1-5)Online publication date: Sep-2020
https://doi.org/10.1109/ATSIP49331.2020.9231888
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Index Terms

Large scale data mining using genetics-based machine learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 Copyright is held by the author/owner(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Fan MXiao KSun LZhang SXu Y(2022)Automated Hyperparameter Optimization of Gradient Boosting Decision Tree Approach for Gold Mineral Prospectivity Mapping in the Xiong’ershan AreaMinerals10.3390/min1212162112:12(1621)Online publication date: 16-Dec-2022
https://doi.org/10.3390/min12121621
Sood ISharma V(2020)A comparative study of Distributed Large Scale Data Mining AlgorithmsBSSS Journal of Computer10.51767/jc1102Online publication date: 25-May-2020
https://doi.org/10.51767/jc1102
Lahmer HOueslati ALachiri Z(2020)Classification of DNA Microarrays Using Deep Learning to identify Cell Cycle Regulated Genes2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)10.1109/ATSIP49331.2020.9231888(1-5)Online publication date: Sep-2020
https://doi.org/10.1109/ATSIP49331.2020.9231888
Lahmer HOueslati ALachiri Z(2019)DNA Microarray Analysis Using Machine Learning to Recognize Cell Cycle Regulated Genes2019 International Conference on Control, Automation and Diagnosis (ICCAD)10.1109/ICCAD46983.2019.9037868(1-5)Online publication date: Jul-2019
https://doi.org/10.1109/ICCAD46983.2019.9037868
Garcia-Piquer AFornells ABacardit JOrriols-Puig AGolobardes E(2014)Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary ClusteringIEEE Transactions on Evolutionary Computation10.1109/TEVC.2013.228151318:1(36-53)Online publication date: Feb-2014
https://doi.org/10.1109/TEVC.2013.2281513
Liu XWang XMatwin SJapkowicz N(2013)Meta-learning for large scale machine learning with MapReduce2013 IEEE International Conference on Big Data10.1109/BigData.2013.6691741(105-110)Online publication date: Oct-2013
https://doi.org/10.1109/BigData.2013.6691741

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