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A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing

A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing

Man Tianxing, Ildar Raisovich Baimuratov, Natalia Alexandrovna Zhukova
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 19
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781522571995|DOI: 10.4018/IJERTCS.2019100102
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MLA

Tianxing, Man, et al. "A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing." IJERTCS vol.10, no.4 2019: pp.20-38. http://doi.org/10.4018/IJERTCS.2019100102

APA

Tianxing, M., Baimuratov, I. R., & Zhukova, N. A. (2019). A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 10(4), 20-38. http://doi.org/10.4018/IJERTCS.2019100102

Chicago

Tianxing, Man, Ildar Raisovich Baimuratov, and Natalia Alexandrovna Zhukova. "A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 10, no.4: 20-38. http://doi.org/10.4018/IJERTCS.2019100102

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Abstract

With the development of the Big Data, data analysis technology has been actively developed, and now it is used in various subject fields. More and more non-computer professional researchers use machine learning algorithms in their work. Unfortunately, datasets can be messy and knowledge cannot be directly extracted, which is why they need preprocessing. Because of the diversity of the algorithms, it is difficult for researchers to find the most suitable algorithm. Most of them choose algorithms through their intuition. The result is often unsatisfactory. Therefore, this article proposes a recommendation system for data processing. This system consists of an ontology subsystem and an estimation subsystem. Ontology technology is used to represent machine learning algorithm taxonomy, and information-theoretic based criteria are used to form recommendations. This system helps users to apply data processing algorithms without specific knowledge from the data science field.

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