Data Mining for Automatic Linguistic Description of Data - Textual Weather Prediction as a Classification Problem

J. Janeiro, I. Rodriguez-Fdez, A. Ramos-Soto, A. Bugarín

2015

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 Harvard Style

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 2: ICAART, ISBN 978-989-758-074-1, pages 556-562. DOI: 10.5220/0005282905560562


in Bibtex Style

@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 2: ICAART,},
year={2015},
pages={556-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005282905560562},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Data Mining for Automatic Linguistic Description of Data - Textual Weather Prediction as a Classification Problem
SN - 978-989-758-074-1
AU - Janeiro J.
AU - Rodriguez-Fdez I.
AU - Ramos-Soto A.
AU - Bugarín A.
PY - 2015
SP - 556
EP - 562
DO - 10.5220/0005282905560562