Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

Authors

  • Virendra Tiwari  Assistant Professor, Department of Computer Science & Application, AKS University, Satna, Madhya Pradesh, India
  • Balendra Garg  Assistant Professor, Department of Computer Science & Application, AKS University, Satna, Madhya Pradesh, India
  • Uday Prakash Sharma  Lab Instructor, Department of Computer Science & Application, AKS University, Satna, Madhya Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT206133

Keywords:

Machine learning algorithms, Support vector machines, Deep Learning.

Abstract

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

References

  1. P. Louridas and C. Ebert, "Machine learning and its applications: A review", International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), vol: 33, Issue: 5, Sept.-Oct. 2016.
  2. A. Dey, "Machine Learning Algorithms: A Review", International Journal of Computer Science and Information Technologies, Vol. 7 (3), 2016.
  3. Teng Xiuyi and Gong Yuxia, “Research on Application of Machine Learning in Data Mining”. IOP Conf. Series: Materials Science and Engineering, 2018.
  4. M. Praveena and V. Jaiganesh, “Literature Review on Supervised Machine Learning Algorithms and Boosting Process”. International Journal of Computer Applications, ISSN No. 0975– 8887, vol. 169, 2017.
  5. K. Das and R. N. Behera, “A Survey on Machine learning: Concept, Algorithms and Applications”, International Journal of Innovative Research in Computer and communication Engineering. vol. 5, 2017.
  6. S.B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”, Informatica. pp 249-268, 2007.
  7. Rob Law, "Room occupancy rate forecasting: a neural network approach", International Journal of Contemporary Hospitality Management, vol. 10 Issue 6, pp 234 – 239, 1998.
  8. Z. Hua and B. Zhang, “A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts”, Applied Mathematics and Computation 181, pp 1035–1048, 2006.
  9. R. Carbonneau, K. Laframboise and R. Vahidov, “Application of machine learning techniques for supply chain demand forecasting”, European Journal of Operational Research 184, pp 1140 1154, 2008.
  10. K.Y. Chen and C.H. Wang, “Support vector regression with genetic algorithms in forecasting tourism demand”, Tourism Management 28, pp 215–226, 2007.
  11. W. C. Hong, Y. Dong, and S.Y. Wei, “SVR with hybrid chaotic genetic algorithms for tourism demand forecasting”, Applied Soft Computing 11, pp 1881– 1890, 2011.
  12. E. Hadavandi and B. Turksen, “A Hybrid Fuzzy Intelligent Agent-Based System for Stock Price Prediction”, International Journal Of Intelligent Systems, Vol. 00, pp 1–23, 2012.
  13. J.Shahrabi, E. Hadavandi and S. Asadi, “Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series”, Knowledge-Based Systems 43, pp 112–122, 2013.
  14. K.C. Hung and K.P. Lin, “Tourism demand forecasting using novel hybrid system”, Expert Systems with Applications 41, pp 3691–3702, 2014.
  15. G.B. Huang, “An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels”, Springer, 2014.
  16. X. Sun, W. Sun, and Y. Gao, “Using a Greye Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China”, Tourism Management 52, 2016.
  17. R. Barzegar and B. O. Zielinski, “Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model”, Springer, ISBN 00477-017-1394, 2017.
  18. A. Fouilloy and C.Voyant, “Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability”, Energy 165, 2018.
  19. Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward”, PLoS ONE 13, 2018.
  20. Yu Zhang, Yu Wang, and A. Cichocki, “Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces”, Expert Systems With Applications 96, pp 302–310, 2018.
  21. P. Domingos, “A Few useeful Things to Know about Machin Learning”, 2012.
  22. Y. Singh, P. K. Bhatia and O.P Sangwan, “A review of studies in machine learning technique”. International Journal of Computer Science and Security, vol.1, pp 70 – 84, 2007.
  23. Petersp, “The Need for Machine Learning is everywhere”, March 2015.
  24. J. Brownlee, “A Tour of Machine Learning algorithms” November 25, 2013.
  25. J. Brownlee, “Machine Learning Tools”, December 28, 2015.
  26. T. Ayodele, “Types of Machine Learning algorithms”, 2010.
  27. T. Xiuyi1 and G. Yuxia1, Research on Application of Machine Learning in Data Mining, IOP Conf. Series: Materials Science and Engineering 392 (2018).
  28. B.A. Kitchenham, “Guidelines for performing systematic literature review in software engineering”, Technical report EBSE-2007-001, UK, 2007.
  29. I. H. Witten, E. Frank, M. A. Hall and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, San Mateo, CA:Morgan Kaufmann, 2016.
  30. D. Singh and C. K. Reddy, "A survey on platforms for big data analytics", J. Big Data, vol. 2, no. 1, pp. 1-20, 2015.
  31. P. D. C. de Almeida and J. Bernardino, "Big data open source platforms", Proc. IEEE Int. Congr. Big Data, pp. 268-275, Jun. 2015.
  32. X. Wu, X. Zhu, G.-Q. Wu and W. Ding, "Data mining with big data", IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97-107, Jan. 2014.
  33. Z. Yang and D. Li, "Application of Logistic Regression with Filter in Data Classification", Chinese Control Conference (CCC), 8865281, 30 July 2019.
  34. T. Rymarczyk, E. Kozłowski, G. Kłosowski, and Konrad Niderla1, "Logistic Regression for Machine Learning in Process Tomography", Sensors. doi: 10.3390/s19153400. Aug 2019.
  35. E. Christodoulou, J. Ma and E. W. Steyerberg, "A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models Auth", Journal of Clinical Epidemiology, Vol. 110, June 2019, Pages 12-22
  36. X. Zhang, Di Wang, and Y. Yang, "Prediction accuracy analysis with logistic regression and CART decision tree", Fourth International Workshop on Pattern Recognition, vol. 11198 Nanjing-China,31 July 2019

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Virendra Tiwari, Balendra Garg, Uday Prakash Sharma, " Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.458-467, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206133