Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

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
 ...