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A Parallel Implementation of Information Gain Using Hive in Conjunction with MapReduce for Continuous Features

  • Sikha BaguiEmail author
  • Sharon K. John
  • John P. Baggs
  • Subhash Bagui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)

Abstract

Finding efficient ways to perform the Information Gain algorithm is becoming even more important as we enter the Big Data era where data and dimensionality are increasing at alarming rates. When machine learning algorithms get over-burdened with large dimensional data with redundant features, information gain becomes very crucial for feature selection. Information gain is also often used as a pre-cursory step in creating decision trees, text classifiers, support vector machines, etc. Due to the very large volume of today’s data, there is a need to efficiently parallelize classic algorithms like Information Gain. In this paper, we present a parallel implementation of Information Gain in the MapReduce environment, using MapReduce in conjunction with Hive, for continuous features. In our approach, Hive was used to calculate the counts and parent entropy and a Map only job was used to complete the Information Gain calculations. Our approach demonstrated gains in run times as we carefully designed MapReduce jobs efficiently leveraging the Hadoop cluster.

Keywords

Hadoop Hive Information Gain Parallel implementation Feature selection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sikha Bagui
    • 1
    Email author
  • Sharon K. John
    • 1
  • John P. Baggs
    • 1
  • Subhash Bagui
    • 1
  1. 1.University of West FloridaPensacolaUSA

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