Research on feature coding theory and typical application analysis in machine learning algorithms

Research Article
Open access

Research on feature coding theory and typical application analysis in machine learning algorithms

Pengxiang Wang 1 , Kailiang Xiao 2* , Lihao Zhou 3
  • 1 JSNU-SPbPU Institute of Engineering, Jiangsu Normal University, Xuzhou, 221116, China    
  • 2 School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China    
  • 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China    
  • *corresponding author 202030242188@mail.scut.edu.cn
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/32/20230188
ACE Vol.32
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-289-3
ISBN (Online): 978-1-83558-290-9

Abstract

Nowadays, the world is still in the environment of economic depression. In order to promote economic recovery, improve Relations of production and production efficiency, stimulate consumption expansion and upgrading, and accelerate industrial transformation and upgrading, problems such as industrial upgrading need to be solved urgently. Solving the above problems requires more useful tools, and artificial intelligence is one of them. Machine learning is the key to distinguishing artificial intelligence from ordinary program code. Unlike people learning knowledge, machine learning has its own unique language algorithms and behavioral logic. Machine learning, as a technology active in the field of artificial intelligence in recent years, specializes in studying how computers learn, simulate and realize part of human learning behavior, so as to provide data mining and behavior prediction for humans, to obtain new knowledge or skills, or to strengthen the original basic ability of machines. In this study, a variety of common coding algorithms and learning strategies in machine learning are discussed, supervised learning algorithms are selected as examples in the learning strategies, models are further selected and evaluated for a variety of algorithms, and parameters are adjusted and performance is analyzed. As for the theoretical analysis in the research, the paper makes a tentative application in the three fields of housing price, physical store sales and digital recognition, explores and selects the corresponding application method in the appropriate scenario, and expands the application field of machine learning.

Keywords:

artificial intelligence, machine learning, feature encoding

Wang,P.;Xiao,K.;Zhou,L. (2024). Research on feature coding theory and typical application analysis in machine learning algorithms. Applied and Computational Engineering,32,85-92.
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References

[1]. Sanni-Anibire M O, Zin R M, Olatunji S O. Developing a preliminary cost estimation model for tall buildings based on machine learning[M]//Big Data and Information Theory. Routledge, 2022: 94-102.

[2]. Canese L, Cardarilli G C, Di Nunzio L, et al. Multi-agent reinforcement learning: A review of challenges and applications[J]. Applied Sciences, 2021, 11(11): 4948.

[3]. Sarker I H. Machine learning: Algorithms, real-world applications and research directions[J]. SN computer science, 2021, 2(3): 160.

[4]. Li Y. Research and application of deep learning in image recognition[C]//2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2022: 994-999.

[5]. Sun Q, Ge Z. A survey on deep learning for data-driven soft sensors[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5853-5866.

[6]. Wang P, Fan E, Wang P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning[J]. Pattern Recognition Letters, 2021, 141: 61-67.

[7]. Ginart A A, Naumov M, Mudigere D, et al. Mixed dimension embeddings with application to memory-efficient recommendation systems[C]//2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021: 2786-2791.

[8]. Zhang W, Li H, Li Y, et al. Application of deep learning algorithms in geotechnical engineering: a short critical review[J]. Artificial Intelligence Review, 2021: 1-41.

[9]. Jia W, Sun M, Lian J, et al. Feature dimensionality reduction: a review[J]. Complex & Intelligent Systems, 2022, 8(3): 2663-2693.

[10]. Zhang Y, Shi X, Zhang H, et al. Review on deep learning applications in frequency analysis and control of modern power system[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107744.

Cite this article

Wang,P.;Xiao,K.;Zhou,L. (2024). Research on feature coding theory and typical application analysis in machine learning algorithms. Applied and Computational Engineering,32,85-92.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-289-3(Print) / 978-1-83558-290-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.32
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Sanni-Anibire M O, Zin R M, Olatunji S O. Developing a preliminary cost estimation model for tall buildings based on machine learning[M]//Big Data and Information Theory. Routledge, 2022: 94-102.

[2]. Canese L, Cardarilli G C, Di Nunzio L, et al. Multi-agent reinforcement learning: A review of challenges and applications[J]. Applied Sciences, 2021, 11(11): 4948.

[3]. Sarker I H. Machine learning: Algorithms, real-world applications and research directions[J]. SN computer science, 2021, 2(3): 160.

[4]. Li Y. Research and application of deep learning in image recognition[C]//2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2022: 994-999.

[5]. Sun Q, Ge Z. A survey on deep learning for data-driven soft sensors[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5853-5866.

[6]. Wang P, Fan E, Wang P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning[J]. Pattern Recognition Letters, 2021, 141: 61-67.

[7]. Ginart A A, Naumov M, Mudigere D, et al. Mixed dimension embeddings with application to memory-efficient recommendation systems[C]//2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021: 2786-2791.

[8]. Zhang W, Li H, Li Y, et al. Application of deep learning algorithms in geotechnical engineering: a short critical review[J]. Artificial Intelligence Review, 2021: 1-41.

[9]. Jia W, Sun M, Lian J, et al. Feature dimensionality reduction: a review[J]. Complex & Intelligent Systems, 2022, 8(3): 2663-2693.

[10]. Zhang Y, Shi X, Zhang H, et al. Review on deep learning applications in frequency analysis and control of modern power system[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107744.

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