MRCS: Map Reduce based Algorithm for Identifying Important Features from Big Data using Chi-Square Test
Chandrashekar D. K.1, Srikantaiah K. C.2, Venugopal K. R.3
1Chandrashekar D. K, Assistant Professor, Department of Computer Science and Engineering, SJB Institute of Technology, Bangalore (Karnataka), India.
2Srikantaiah K. C, Professor, Department of Computer Science and Engineering, SJB Institute of Technology, Bangalore (Karnataka), India.
3Venugopal K. R, Vice-Chancellor of Bangalore University, Bangalore (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 497-501 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11301292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1130.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Big Data, Business Intelligence Rules, Chi-Square, Feature Selection, Map-Reduce.
Scope of the Article: Big Data Networking