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Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data

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Abstract

Big data is the recent imminent technology, which can provide large benefits to the business administration. Owing to such huge volume, it becomes very complicated to ensure effective analysis by the existing techniques. The complications can be related to analyze, capture, sharing, storage, and visualization of the data. To tackle these challenges, a novel classification technique using Holoentropy based Correlative Naive Bayes classifier and MapReduce Model (HCNB-MRM) is proposed. The proposed HCNB, which is designed by combining the Holoentropy function with the correlative based Naive Bayes classifier deals with both high-dimensional data sets as well as extensive datasets to improve the benchmark, and classify the data based on dependent assumption. Therefore, the proposed HCNB-MRM is used to make the process simpler and to choose the best features from big dataset. The proposed HCNB with the MapReduce Model maximizes the performance of big data classification using probability index table, and posterior probability of the testing data samples. The performance of the proposed HCNB-MRM is evaluated using three metrics, such as accuracy, sensitivity, and specificity. From the experimental results, it is analyzed that the proposed HCNB-MRM obtains a high classification accuracy of 93.5965% and 94.3369% for the localization dataset, and skin dataset when compared with the existing techniques.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India

    Chitrakant Banchhor & N. Srinivasu

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  1. Chitrakant Banchhor
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  2. N. Srinivasu
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Correspondence to Chitrakant Banchhor.

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Banchhor, C., Srinivasu, N. Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data. Evol. Intel. 15, 1037–1050 (2022). https://doi.org/10.1007/s12065-019-00276-9

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  • DOI: https://doi.org/10.1007/s12065-019-00276-9

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