Open AccessMRCS: Map Reduce based Algorithm for Identifying Important Features from Big Data using Chi-Square Test
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1130.1292s19
Subject(s) - computer science , big data , data pre processing , feature selection , preprocessor , data mining , business intelligence , square (algebra) , feature (linguistics) , analytics , artificial intelligence , test data , volume (thermodynamics) , feature extraction , selection (genetic algorithm) , machine learning , pattern recognition (psychology) , mathematics , linguistics , philosophy , physics , geometry , quantum mechanics , programming language
In recent trend, big data analytics is a hot research topic for analyzing data for the business purposes, in which extraction of the important features from high volume of data is a hindrance job. In the current system, there are various methods available to extract the important feature, but it is not accurate in extraction of important features. To overcome this problem, in this paper, we have proposed a model called Map- Reduce based Chi-Square (MRCS) for feature selection. Next, the data preprocessing techniques and machine learning algorithms are used to generate business intelligence rules. The experimental results show that our proposed algorithm takes less execution time.