A Survey of ML Techniques and Tools

Rajan Verma

Abstract


In today's society, a large amount of electronic data is created in every sector. These data sets provide information that can be used to forecast the future. Manual forecasting is a difficult activity for humans due to its large size. To solve this challenge, use training and test datasets to train the computer to predict the future on its own. Computer learning methods and tools of many types are available to train the machine. This study focuses on a detailed overview of a few machine learning algorithms and techniques utilized in a variety of applications and areas.

Full Text:

PDF

References


Jafar Tanha, "Semi-supervised self-training for decision tree classifiers", International Journal of Machine Learning and Cybernetics, vol. 8, no. 1, pp. 355-370, January 2015.

D Khadim, M Fleur and D Gayo, "Large scale biomedical texts classification: a k-NN and an ESA-based approaches", Journal of Biomedical Semantics, vol. 7, pp. 40, June 2016.

R Hong, H.M. Wang and L Jian, "Privacy-Preserving k-Nearest Neighbor Computation in Multiple Cloud Environments", IEEE Access, vol. 4, pp. 9589-9603, December 2016, ISSN 2169-3536.

L. Jiang and C. Li, "Deep feature weighting for naive Bayes and its application to text classification", Journal of Engineering Applications of Artificial Intelligence, vol. 52, pp. 26-39, June 2016.

M Ahmed and H Alison, Modeling built-up expansion and densification with multinomial logistic regression cellular automata and genetic algorithm, vol. 67, pp. 147-156, January 2018.

T. Razzaghi and R Oleg, "Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values", PLUS ONE, pp. 1-18, May 2016.

L Hui and D. Pi, "Integrative Method Based on Linear Regression for the Prediction of Zinc binding Sites in Proteins", IEEE Access, vol. 5, pp. 14647-14656, August 2017.

L. Wang and D. Wang, "Intelligent CFAR Detector Based on Support Vector Machine", IEEE Access, vol. 5, pp. 26965-26972, December 2017.

Gupta, K., & Jiwani, N. (2020). Effects of COVID-19 risk controls on the Global Supply Chain. Transactions on Latest Trends in Artificial Intelligence, 1(1).


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: Under Evaluation (2023)

A Double-Blind Peer Reviewed Refereed Journal