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Parallel Fuzzy Cognitive Map Using Evolutionary Feature Reduction for Big Data Classification Problem

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Social Transformation – Digital Way (CSI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 836))

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Abstract

Big data classification is a challenging task because of the enormous volume, variety velocity associated with it. As the amount of data increases it is more difficult for data scientist in collecting, cleaning and analyzing data. To find useful and meaningful data from unstructured data is an important task. Meaning full data can be found using different classification techniques. There are different techniques used so far to gain useful knowledge from big data such as K-Means clustering algorithm, Association rule mining algorithm, linear regression algorithms, logistic regression algorithms, Naïve Bayesian etc. Fuzzy Cognitive Maps (FCM) is another efficient approach which is being used for decision making. The difficulty of using FCMs for big data classification is with the number of large available parameters associated with the data set. Hence in this paper we propose a parallel fuzzy cognitive map using map Reduce framework which learns and classifies from a reduced feature set using parallel evolutionary genetic algorithm. The methodology is tested on Bench Mark Data sets and results show the efficiency of the method.

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Authors and Affiliations

  1. Department of Computer Applications, Cochin University of Science and Technology, Cochin, Kerala, India

    M. V. Judy & Gayathri Soman

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  1. M. V. Judy
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  2. Gayathri Soman
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Correspondence to M. V. Judy .

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Editors and Affiliations

  1. Kalyani University, Kalyani, India

    Jyotsna Kumar Mandal

  2. University of Calcutta, Kolkata, India

    Devadatta Sinha

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Judy, M.V., Soman, G. (2018). Parallel Fuzzy Cognitive Map Using Evolutionary Feature Reduction for Big Data Classification Problem. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_22

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  • DOI: https://doi.org/10.1007/978-981-13-1343-1_22

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  • Online ISBN: 978-981-13-1343-1

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