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Artificial Intelligence for Big Data

You're reading from   Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence techniques

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788472173
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Anand Deshpande Anand Deshpande
Author Profile Icon Anand Deshpande
Anand Deshpande
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Big Data and Artificial Intelligence Systems 2. Ontology for Big Data FREE CHAPTER 3. Learning from Big Data 4. Neural Network for Big Data 5. Deep Big Data Analytics 6. Natural Language Processing 7. Fuzzy Systems 8. Genetic Programming 9. Swarm Intelligence 10. Reinforcement Learning 11. Cyber Security 12. Cognitive Computing 13. Other Books You May Enjoy

Attribute search with genetic algorithms in Weka


Once again, let's select the diabetes dataset in the Preprocess menu and navigate to the Select Attributes menu. In the Search Method selection box, select Genetic Search. The configuration parameters for the Genetic Search can be set by right-clicking the Search Method text. As seen earlier in this chapter, we can tune various parameters of the algorithm and experiment with optimum performance. Here is a screenshot representing Genetic Search with Weka:.

Once we click on the Start button, the algorithm searches through the training data and selects the relevant attributes with GA. Here is the output from the GA execution on the diabetes dataset:

=== Run information ===

Evaluator: weka.attributeSelection.CfsSubsetEval -P 1 -E 1
Search: weka.attributeSelection.GeneticSearch -Z 20 -G 20 -C 0.6 -M 0.033 -R 20 -S 1
Relation: pima_diabetes
Instances: 768
Attributes: 9
              preg
              plas
              pres
              skin
 ...
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