Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data

Authors

  • O. J. Ibidoja
    Department of Mathematics, Federal University Gusau, Gusau, Nigeria; School of Mathematical Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia
  • F. P. Shan
    School of Mathematical Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia
  • Mukhtar
    I-CEFORY (Local Food Innovation), Universitas Sultan Ageng Tirtayasa Indoneia
  • J. Sulaiman
    School of Science and Technology, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
  • M. K. M. Ali
    School of Mathematical Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia

Keywords:

Robust method, Hybrid model, Machine learning, Outliers, Big data

Abstract

A common problem in regression analysis using ordinary least squares (OLS) is the effect of outliers or contaminated data on the estimates of the parameters. A robust method that is not sensitive to outliers and can handle contaminated data is needed. In this study, the objective is to determine the significant parameters that determine the moisture content of the seaweed after drying and develop a hybrid model to reduce the outliers. The data were collected with sensors from the v-Groove Hybrid Solar Drier (v-GHSD) at Semporna, South-Eastern Coast of Sabah, Malaysia. After the second order interaction, we have 435 drying parameters, each parameter has 1914 observations. First, we used four machine learning algorithms, such as random forest, support vector machine, bagging and boosting to determine the significant parameters by selecting 15, 25, 35 and 45 parameters. Second, we developed the hybrid model using robust methods such as M. Bi-Square, M. Hampel and M. Huber. The results show that there is a significant improvement in the reduction of the number of outliers and better prediction using hybrid model for the contaminated seaweed big data. For the highest variable importance of 45 significant drying parameters of seaweed, the hybrid model bagging M Bi-square performs better because it has the lowest percentage of outliers of 4.08 %.

Dimensions

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Published

2023-02-04

How to Cite

Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data. (2023). Journal of the Nigerian Society of Physical Sciences, 5(1), 1137. https://doi.org/10.46481/jnsps.2023.1137

Issue

Volume 5, Issue 1, February 2023

Section

Original Research
Copyright & Licensing

Copyright (c) 2023 O. J. Ibidoja, F. P. Shan, Mukhtar, J. Sulaiman, M. K. M. Ali

Creative Commons License

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

How to Cite

Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data. (2023). Journal of the Nigerian Society of Physical Sciences, 5(1), 1137. https://doi.org/10.46481/jnsps.2023.1137

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