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A brief comparative study of the potentialities and limitations of machine-learning algorithms and statistical techniques
- Source :
- E3S Web of Conferences, Vol 266, p 02001 (2021)
- Publication Year :
- 2021
- Publisher :
- EDP Sciences, 2021.
-
Abstract
- Machine learning is a popular way to find patterns and relationships in high complex datasets. With the nowadays advancements in storage and computational capabilities, some machine-learning techniques are becoming suitable for real-world applications. The aim of this work is to conduct a comparative analysis of machine learning algorithms and conventional statistical techniques. These methods have long been used for clustering large amounts of data and extracting knowledge in a wide variety of science fields. However, the central knowledge of the different methods and their specific requirements for the data set, as well as the limitations of the individual methods, are an obstacle for the correct use of these methods. New machine learning algorithms could be integrated even more strongly into the current evaluation if the right choice of methods were easier to make. In the present work, some different algorithms of machine learning are listed. Four methods (artificial neural network, regression method, self-organizing map, k-means al-algorithm) are compared in detail and possible selection criteria are pointed out. Finally, an estimation of the fields of work and application and possible limitations are provided, which should help to make choices for specific interdisciplinary analyses.
- Subjects :
- Artificial neural network
Computer science
business.industry
020209 energy
02 engineering and technology
Machine learning
computer.software_genre
Variety (cybernetics)
Environmental sciences
Data set
Obstacle
0202 electrical engineering, electronic engineering, information engineering
Selection (linguistics)
GE1-350
020201 artificial intelligence & image processing
Artificial intelligence
Cluster analysis
business
Algorithm
computer
Subjects
Details
- ISSN :
- 22671242
- Volume :
- 266
- Database :
- OpenAIRE
- Journal :
- E3S Web of Conferences
- Accession number :
- edsair.doi.dedup.....1dc147a78c5d33c792a2403f82adfc79
- Full Text :
- https://doi.org/10.1051/e3sconf/202126602001