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Authors: Krzysztof Wróbel 1 ; Maciej Wielgosz 2 ; Marcin Pietron 3 ; Michal Karwatowski 4 ; Jerzy Duda 4 and Aleksander Smywinski-Pohl 4

Affiliations: 1 Jagiellonian University and AGH University of Science and Technology, Poland ; 2 AGH University of Science and Technology and Academic Computer Centre CYFRONET, Poland ; 3 Academic Computer Centre CYFRONET and AGH University of Science and Technology, Poland ; 4 AGH University of Science and Technology, Poland

Keyword(s): Precision Reduction, Text Classification, SVD.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Natural Language Processing ; Pattern Recognition ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification. The precision reduction of the vectors representing the data (e.g. TF–IDF representation in our case) allows for a decrease of computing time and memory footprint on dedicated hardware platforms. The impact of precision reduction on the classification quality was performed on 5 corpora, using 4 different classifiers. Also, dimensionality reduction was taken into account. Results indicate that the precision reduction improves classification accuracy for most cases (up to 25% of error reduction). In general, the reduction from 64 to 4 bits gives the best scores and ensures that the results will not be worse than with the full floating-point representation.

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Paper citation in several formats:
Wróbel, K.; Wielgosz, M.; Pietron, M.; Karwatowski, M.; Duda, J. and Smywinski-Pohl, A. (2018). Improving Text Classification with Vectors of Reduced Precision. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 531-538. DOI: 10.5220/0006641505310538

@conference{icaart18,
author={Krzysztof Wróbel. and Maciej Wielgosz. and Marcin Pietron. and Michal Karwatowski. and Jerzy Duda. and Aleksander Smywinski{-}Pohl.},
title={Improving Text Classification with Vectors of Reduced Precision},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={531-538},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006641505310538},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Improving Text Classification with Vectors of Reduced Precision
SN - 978-989-758-275-2
IS - 2184-433X
AU - Wróbel, K.
AU - Wielgosz, M.
AU - Pietron, M.
AU - Karwatowski, M.
AU - Duda, J.
AU - Smywinski-Pohl, A.
PY - 2018
SP - 531
EP - 538
DO - 10.5220/0006641505310538
PB - SciTePress

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