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Authors: J. Janeiro ; I. Rodriguez-Fdez ; A. Ramos-Soto and A. Bugarín

Affiliation: University of Santiago de Compostela, Spain

Keyword(s): Linguistic Descriptions of Data, Natural Language Generation, Weather Forecasting, Classification.

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

Abstract: In this paper we present the results and performance of five different classifiers applied to the task of automatically generating textual weather forecasts from raw meteorological data. The type of forecasts this methodology can be applied to are template-based ones, which can be transformed into an intermediate language that can directly mapped to classes (or values of variables). Experimental validation and tests of statistical significance were conducted using nine datasets from three real meteorological publicly accessible websites, showing that RandomForest, IBk and PART are statistically the best classifiers for this task in terms of F-Score, with RandomForest providing slightly better results.

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Paper citation in several formats:
Janeiro, J.; Rodriguez-Fdez, I.; Ramos-Soto, A. and Bugarín, A. (2015). Data Mining for Automatic Linguistic Description of Data - Textual Weather Prediction as a Classification Problem. In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-074-1; ISSN 2184-433X, SciTePress, pages 556-562. DOI: 10.5220/0005282905560562

@conference{icaart15,
author={J. Janeiro. and I. Rodriguez{-}Fdez. and A. Ramos{-}Soto. and A. Bugarín.},
title={Data Mining for Automatic Linguistic Description of Data - Textual Weather Prediction as a Classification Problem},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2015},
pages={556-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005282905560562},
isbn={978-989-758-074-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Data Mining for Automatic Linguistic Description of Data - Textual Weather Prediction as a Classification Problem
SN - 978-989-758-074-1
IS - 2184-433X
AU - Janeiro, J.
AU - Rodriguez-Fdez, I.
AU - Ramos-Soto, A.
AU - Bugarín, A.
PY - 2015
SP - 556
EP - 562
DO - 10.5220/0005282905560562
PB - SciTePress

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