Examination of ML use of rough sets theory

Pulkit jain

Abstract


Machine learning is a large subject of artificial intelligence that focuses on the creation and improvement of algorithms and techniques that allow computers to "learn." Machine learning has a wide range of uses, and academics have devoted a lot of time and energy to studying it. However, the challenge of quantifying learning quality and supervisor knowledge under partial information has not been handled particularly effectively. Uncertain and partial information may be used to derive knowledge using rough sets theory.

References


An Zengbo, Zhang Yan. "The Application Study of Machine Learning". Journal of Changzhi University, Vol.24,No.2, 2007 pp.21-24

LI Shi-zhuo, YAN Man-fu. "Machine Learning Problems Based on Data". Journal of Tangshan Teachers College,2007.9 pp.66-67

Aboul Ella Hassanien. "Intelligent Data Analysis of Breast Cancer Based on Rough Sets Theory". International Journal on Artificial Intelligence Tools, Vol.12, No.4 (2003), pp.465-479.

Z. Punja, "Flower and foliage-infecting pathogens of marijuana (Cannabis sativa L.) plants", Canadian Journal of Plant Pathology, vol. 40, no. 4, pp. 514-527, 2018.

Z. Punja, D. Collyer, C. Scott, S. Lung, J. Holmes and D. Sutton, "Pathogens and Molds Affecting Production and Quality of Cannabis sativa L", Frontiers in Plant Science, vol. 10, 2019.

P. Cockson, H. Landis, T. Smith, K. Hicks and B. Whipker, Characterization of Nutrient Disorders of Cannabis sativa", Applied Sciences, vol. 9, no. 20, pp. 4432, 2019.

P.Fabian, V. Gaël, G. Alexandre, M. Vincent, T. Bertrand, G. Olivier et al., "Scikit-learn: Machine Learning in Python", Journal of Machine Learning Research, pp. 2825-2830, 2011.

K. M. Lee, K. Y. Kim and J. S. Yoo, "Autonomicity Levels and requirements for Automated Machine Learning", Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 46-48, 2017, Sept.

Gupta, K., & Jiwani, N. (2021). A systematic Overview of Fundamentals and Methods of Business Intelligence. International Journal of Sustainable Development in Computing Science, 3(3), 31-46.

Tomar, U., Chakroborty, N., Sharma, H., & Whig, P. (2021). AI based Smart Agricuture System. Transactions on Latest Trends in Artificial Intelligence, 2(2).

WHIG, P. (2021). Innovative Smart Blind Guidance System Based on IoT. Transactions on Latest Trends in IoT, 3(3).

WHIG, P. (2019). Application of Machine learning to investigate the mortality risk of viral diseases. Transactions on Latest Trends in IoT, 1(1).

Velu, A. (2019). The spread of big data science throughout the globe. International Journal of Sustainable Development in Computing Science, 1(1), 11-20.

Velu, A. (2019). A Stable Pre-processing Method for the Handwritten Recognition System. International Journal of Machine Learning for Sustainable Development, 1(1), 21-30.

Whig, P. (2019). Exploration of Viral Diseases mortality risk using machine learning. International Journal of Machine Learning for Sustainable Development, 1(1), 11-20.

Whig, P. (2019). A Novel Multi-Center and Threshold Ternary Pattern. International Journal of Machine Learning for Sustainable Development, 1(2), 1-10.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 International Journal of Machine Learning for Sustainable Development

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

Impact Factor : 

JCR Impact Factor: 5.9 (2020)

JCR Impact Factor: 6.1 (2021)

JCR Impact Factor: 6.7 (2022)

JCR Impact Factor: 7.6 (2023)

JCR Impact Factor: 8.6 (2024)

JCR Impact Factor: Under Evaluation (2025)

A Double-Blind Peer-Reviewed Refereed Journal