RT Journal Article
T1 Meta-heuristics for generation of linguistic descriptions of weather data: experimental comparison of two approaches
A1 Cascallar Fuentes, Andrea
A1 Ramos Soto, Alejandro
A1 Bugarín-Diz, Alberto
K1 Linguistic descriptions of data
K1 Data-to-text systems
K1 Computing with words
K1 Natural language generation
AB In this paper we experimentally assess, from both algorithmic and pragmatic perspectives, the adequacy of linguistic descriptions of real data generated by two metaheuristics: simulated annealing and genetic algorithm meta-heuristics. The type of descriptions we consider are fuzzy quantified statements (both Zadeh's type-1 and type-2) involving three well-known quantification models (Zadeh's scalar and fuzzy and Delgado's GD). We conducted an empirical validation using real observation and prediction meteorological data, where both automatic (metrics-based) and manual (human experts-based) assessment on the adequacy of the generated descriptions was assessed. Results indicate that, overall, the genetic approach performs better than simulated annealing in terms of quality of the obtained descriptions and time execution. Significance of this outperforming depends on the type of meteorological data and the quantification model selected. Tests of statistical significance point out that for type-1 descriptions no significant differences exist between the two meta-heuristics in the prediction case. For type-2 descriptions, significant differences exist for Delgado's GD model for both types of data. For Zadeh's scalar and fuzzy quantification significance depends on the type of data (observation or prediction). Globally, outperforming of the genetic approach over simulated annealing i) is significant in 4 out of 12 scenarios considered (all of them type-2), and ii) is not significant in the other 8 out 12 scenarios (all type-1 and two type-2). Also human expert assessment on the adequacy of the descriptions was conducted, showing that both meta-heuristics behave similarly for type-1 descriptions, while genetic algorithms produce more suitable type-2 linguistic descriptions
PB Elsevier
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29044
UL http://hdl.handle.net/10347/29044
LA eng
NO Fuzzy Sets and Systems 443 (2022) 173-202. https://doi.org/10.1016/j.fss.2022.02.016
NO This research was funded by the Spanish Ministry for Science, Innovation and Universities (grants TIN2017-84796-C2-1-R, PID2020-112623GB-I00 and PDC2021-121072-C21) and the Galician Ministry of Education, University and Professional Training (grants ED431C2018/29 and ED431G2019/04). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)
DS Minerva
RD 29 abr 2026