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 (Itmo University, St. Petersburg, Russia), Ildar Raisovich Baimuratov (Itmo University, St. Petersburg, Russia) and Natalia Alexandrovna Zhukova (St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), St. Petersburg, Russia)
DOI: 10.4018/IJERTCS.2019100102

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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Background

Provide broad definitions and discussions of the topic and incorporate views of others (literature review) into the discussion to support, refute, or demonstrate your position on the topic.1

Data processing is a complex process. Kotsiantis (2007) provide a comprehensive review about Supervised machine learning. Satyanandam, N., & Satyanarayana, D. C. (2013) describe a taxonomy of ML and data mining for Healthcare Systems. Ayodele, T. O. (2010) represents main type of ML algorithms and their advantages and disadvantages are briefly introduced. But the main points in these reviews are the process of ML algorithms not the selection of algorithms. They are not friendly to non-experts.

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