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Efficient Big Data Classification through Distributed Kernel-Based Extreme Learning Machine Approach


Affiliations
1 Dept of Computer Science, Karpagam Academy of Higher Education, Coimbatore-641021, Tamil Nadu, India
2 Dept of Computer Application, Karpagam Academy of Higher Education, Coimbatore-641021, Tamil Nadu, India
 

Objectives: The main objective of this work is to provide effective big data classification by analyzing the classification performance of the existing machine learning algorithms.

Methods/Statistical analysis: In MapReduce, after pre-processing and initial partitioning, the features are selected by reducing the dimensionality using the SPARK ITFS technique and then the classification is performed at each node. The Extreme Learning Machine (ELM) is utilized in this work to provide efficient big data classification with reduced computation and network traffic cost. The work is also aimed to reduce the overall memory consumption and reduce time complexity.

Findings: The various research works has been analyzed and evaluated. From the analysis, the classification is performed using machine learning techniques such as Artificial Neural Networks (ANN), Gradient Boosting, Support Vector machine (SVM), Random forests, Naïve Bayes, etc. Though the techniques have good classification performance, the classification performance can be further enhanced by using more advanced machine learning approaches. The proposed Distributed Kernel-ELM (DK-ELM) gives better performance in terms of accuracy, precision, recall and response time compared with existing machine learning algorithms such as SVM, ANN and Partial Least Square-Discriminant Analysis (PLS-DA) utilized in the partition based aggregation methods.

Application/Improvements: The findings of this work prove that the DK-ELM provides better result than other approaches.


Keywords

Big Data Classification, Artificial Neural Networks, Support Vector Machine, Partial least Square-discriminant Analysis, Distributed Kernel-ELM.
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  • J. L., Liu, Y. Bai, J. Kang, N. An. A new approach to hierarchical clustering using partial least squares. In 2006 International Conference on Machine Learning and Cybernetics. IEEE .2006 Aug, pp. 1125-1131.

  • C. Serrano-Cinca, B. Gutiérrez-Nieto. Partial Least Square Discriminant Analysis (PLS-DA) for bankruptcy prediction. Working Papers CEB, 2011, 11.

  • S. C. Huang, T. K. Wu. Robust semi-supervised SVM on kernel partial least discriminant space for high dimensional data mining. In 2012 International Conference on Information Science and Applications. IEEE, 2012, May, pp. 1-6.

  • D. Dehestani, S. Su, H. Nguyen, Y. Guo. Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model. In Proceedings of European Control Conference (ECC), 2013, July, pp. 2976-2981.

  • X. Liu, J.Tang. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Systems Journal. 2014, 8(3), 910-920.

  • F. A. Zaghoul, S. Al-Dhaheri. Arabic Text Classification Based on Features Reduction Using Artificial Neural Networks. In Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference on. IEEE. 2013, April, pp. 485-490

  • P. Sok, N. Taing. Support Vector Machine (SVM) Based Classifier For Khmer Printed Character-set Recognition. In Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, IEEE , 2014 Dec, pp. 1-9.

  • Z. Camlica, H. R. Tizhoosh, F. Khalvati. Medical image classification via svm using lbp features from saliencybased folded data.arXiv preprint arXiv:1509.04619. 2015.

  • C. Yin, J. Xiang, H. Zhang, J. Wang, Z.Yin, J. U. Kim. A New SVM Method for Short Text Classification Based on Semi-Supervised Learning. In 2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS) 2015, IEEE, Aug, pp. 100-103.

  • Y. Zhang, S. Chen, Q. Wang, G.Yu. iMapReduce: Incremental MapReduce for Mining Evolving Big Data. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(7), 1906-1919.

  • S. Kheradmanda, O. Nematollahi, A. R. Ayoobia. Clearness index predicting using an integrated artificial neural network (ANN) approach. Renewable and Sustainable Energy Reviews, 2016, 58, 1357-1365.

  • N. Karthick, X. A. Kalarani. An Effective Filtering on Big Data by Finding Relevant Features to Extract Useful Information. International Journal of Applied Engineering Research, 2016, 11(6), 3805-3810.

  • N. Karthick, X. A. Kalarani. An improved method for handling and extracting useful information from big data. Indian Journal of Science and Technology, 2015, 8(33), 1-7.


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  • Efficient Big Data Classification through Distributed Kernel-Based Extreme Learning Machine Approach

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Authors

N. Karthick
Dept of Computer Science, Karpagam Academy of Higher Education, Coimbatore-641021, Tamil Nadu, India
X. Agnes Kalarani
Dept of Computer Application, Karpagam Academy of Higher Education, Coimbatore-641021, Tamil Nadu, India

Abstract


Objectives: The main objective of this work is to provide effective big data classification by analyzing the classification performance of the existing machine learning algorithms.

Methods/Statistical analysis: In MapReduce, after pre-processing and initial partitioning, the features are selected by reducing the dimensionality using the SPARK ITFS technique and then the classification is performed at each node. The Extreme Learning Machine (ELM) is utilized in this work to provide efficient big data classification with reduced computation and network traffic cost. The work is also aimed to reduce the overall memory consumption and reduce time complexity.

Findings: The various research works has been analyzed and evaluated. From the analysis, the classification is performed using machine learning techniques such as Artificial Neural Networks (ANN), Gradient Boosting, Support Vector machine (SVM), Random forests, Naïve Bayes, etc. Though the techniques have good classification performance, the classification performance can be further enhanced by using more advanced machine learning approaches. The proposed Distributed Kernel-ELM (DK-ELM) gives better performance in terms of accuracy, precision, recall and response time compared with existing machine learning algorithms such as SVM, ANN and Partial Least Square-Discriminant Analysis (PLS-DA) utilized in the partition based aggregation methods.

Application/Improvements: The findings of this work prove that the DK-ELM provides better result than other approaches.


Keywords


Big Data Classification, Artificial Neural Networks, Support Vector Machine, Partial least Square-discriminant Analysis, Distributed Kernel-ELM.

References