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Performance Comparison of Logistic Regression Algorithms on RHadoop
Journal of The Korea Society of Computer and Information
Abbr :
JKSCI
2017, 22(4), pp.9-16
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
2017, 22(4), pp.9-16
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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Machine learning has found widespread implementations and applications in many different domains in our life. Logistic regression is a type of classification in machine leaning, and is used widely in many fields, including medicine, economics, marketing and social sciences. In this paper, we present the MapReduce implementation of three existing algorithms, this is, Gradient Descent algorithm, Cost Minimization algorithm and Newton-Raphson algorithm, for logistic regression on RHadoop that integrates R and Hadoop environment applicable to large scale data. We compare the performance of these algorithms for estimation of logistic regression coefficients with real and simulated data sets. We also compare the performance of our RHadoop and RHIPE platforms. The performance experiments showed that our Newton-Raphson algorithm when compared to Gradient Descent and Cost Minimization algorithms appeared to be better to all data tested, also showed that our RHadoop was better than RHIPE in real data, and was opposite in simulated data.
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