Hadoop based Feature Selection and Decision Making Models on Big Data

Abstract

Objectives: A large amount of informative data is being captured and processed by today’s organizations and is continuing to increase exponentially. It becomes computationally inaccurate to analyze such big data for decision making systems. Methods/Analysis: Hadoop, which is a working model based on the Map-Reduce framework with efficient computation and processing of Big Data. Findings: Most of the traditional classification algorithms have issues such as class imbalance and dimension reduction on Big Data. However, a large part of the data produced today are incomplete and inaccurate, so large organizations prefer relational databases to store their information, but the user query processing speed is very low. Unlike existing solutions that require a prior knowledge of classification accuracy for various types of data characteristics, which is impossible to obtain in practice. Applications/Improvement: In this paper, we have given a compared proposed model to different big data feature selection and classification models along with advantages and limitations. Keywords: Big Data, Decision Tree, Feature Selection, Hadoop, MapReduce