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Feature Selection Algorithms as One of the Python Data Analytical Tools

Information Technologies and Programming Faculty, ITMO University, 197101 St. Petersburg, Russia
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Proceeding of the 25th conference of FRUCT association.
Current address: Kronverkskiy pr.49, 197101, St.Petersburg, Russia.
Future Internet 2020, 12(3), 54;
Received: 31 January 2020 / Revised: 9 March 2020 / Accepted: 12 March 2020 / Published: 16 March 2020
With the current trend of rapidly growing popularity of the Python programming language for machine learning applications, the gap between machine learning engineer needs and existing Python tools increases. Especially, it is noticeable for more classical machine learning fields, namely, feature selection, as the community attention in the last decade has mainly shifted to neural networks. This paper has two main purposes. First, we perform an overview of existing open-source Python and Python-compatible feature selection libraries, show their problems, if any, and demonstrate the gap between these libraries and the modern state of feature selection field. Then, we present new open-source scikit-learn compatible ITMO FS (Information Technologies, Mechanics and Optics University feature selection) library that is currently under development, explain how its architecture covers modern views on feature selection, and provide some code examples on how to use it with Python and its performance compared with other Python feature selection libraries. View Full-Text
Keywords: machine learning; feature selection; open-source library; Python machine learning; feature selection; open-source library; Python
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Pilnenskiy, N.; Smetannikov, I. Feature Selection Algorithms as One of the Python Data Analytical Tools. Future Internet 2020, 12, 54.

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