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

Chapter 8. Genetic Programming

Big Data mining tools need to be empowered by computationally efficient techniques to increase their degree of efficiency. Using genetic algorithms over data mining creates great robust, computationally efficient, and adaptive systems. In fact, with an exponential explosion of data, data analytics techniques go on taking more time and inversely affect the throughput. Also, due to their static nature, complex hidden patterns are often left out. In this chapter, we want to show how to use genes to mine data with great efficiency. To achieve this objective, we are going to explore some of the basics of genetic programming and the fundamental algorithms. We are going to begin with some of the very basic principles of natural (biological) genetics and draw some parallels when it comes to applying the general theory to computer algorithms. We will cover the following:

  • Genetic algorithm structure
  • KEEL framework
  • Encog machine learning framework
  • Weka framework
  • Attribute...