Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data

A. Ramos-Soto, A. Bugarín, S. Barro

2015

Abstract

We are living in a world which is increasingly flooded with vast amounts of data. As a consequence, the use of techniques allowing to exploit and explain the information contained in this raw data has become mandatory. In this context, more human-friendly alternatives to standard techniques like statistics or data mining approaches are being considered. Among them, the soft computing field provides a set of tools allowing the creation of linguistic descriptions of data. These are automatically generated textual explanations that comprise the most relevant information that is implicit in the data, providing linguistic concepts which deal with the imprecision and ambiguity of language through the use of fuzzy sets. Following this research line, the Ph.D. we propose explores the potential of this field by providing real solutions employing linguistic descriptions and also extending the current theoretical base to consider a higher expressiveness.

References

  1. Alvarez-Alvarez, A. and Trivino, G. (2013). Linguistic description of the human gait quality. Engineering Applications of Artificial Intelligence, 26(1):13 - 23.
  2. Castillo-Ortega, R., Marín, N., and Sánchez, D. (2011a). A fuzzy approach to the linguistic summarization of time series. Multiple-Valued Logic and Soft Computing, pages 157-182.
  3. Castillo-Ortega, R., Marín, N., Sánchez, D., and Tettamanzi, A. (2012). Quality assessment in linguistic summaries of data. In Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., and Yager, R., editors, Advances in Computational Intelligence, volume 298 of Communications in Computer and Information Science, pages 285-294. Springer Berlin Heidelberg.
  4. Castillo-Ortega, R., Marín, N., Sánchez, D., and Tettamanzi, A. G. (2011b). Linguistic summarization of time series data using genetic algorithms. In Advances in Intelligent Systems Research, volume 1 - 1, pages 416 - 423.
  5. Coch, J. (1998). Multimeteo: multilingual production of weather forecasts. ELRA Newsletter, 3(2).
  6. Delgado, M., Ruiz, M. D., Sánchez, D., and Vila, M. A. (2014). Fuzzy quantification: a state of the art. Fuzzy Sets and Systems, 242(0):1 - 30. Theme: Quantifiers and Logic.
  7. Diaz-Hermida, F. and Bugarin, A. (2011). Semi-fuzzy quantifiers as a tool for building linguistic summaries of data patterns. In IEEE Symposium on Foundations of Computational Intelligence, pages 45-52.
  8. Díaz-Hermida, F., Ramos-Soto, A., and Bugarín, A. (2011). On the role of fuzzy quantified statements in linguistic summarization. In Proceedings of 11th International Conference on. Intelligent Systems Design and Applications (ISDA), pages 166-171.
  9. Eciolaza, L. and Trivino, G. (2011). Linguistic reporting of driver behavior: Summary and event description. In Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, pages 148- 153.
  10. Gamut, L. T. F. (1991). Logic, Language and Meaning. University of Chicago Press.
  11. Goldberg, E., Driedger, N., and Kittredge, R. (1994). Using natural-language processing to produce weather forecasts. IEEE Expert, 9(2):45-53.
  12. Kacprzyk, J. (2010). Computing with words is an implementable paradigm: Fuzzy queries, linguistic data summaries, and natural-language generation. IEEE Trans. Fuzzy Systems, pages 451-472.
  13. Kacprzyk, J. and Wilbik, A. (2009). Using fuzzy linguistic summaries for the comparison of time series: an application to the analysis of investment fund quotations. In Proceedings IFSA/EUSFLAT Conf. 2009, pages 1321-1326.
  14. Kacprzyk, J. and Zadrozny, S. (2005). Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools. Inf. Sci. Inf. Comput. Sci., 173(4):281-304.
  15. Kacprzyk, J. and Zadrozny, S. (2010). Linguistic data summarization: A high scalability through the use of natural language? Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, pages 214-237.
  16. Kobayashi, I. and Okumura, N. (2009). Verbalizing timeseries data: With an example of stock price trends. In Proceedings IFSA/EUSFLAT Conf., pages 234-239.
  17. Menendez, C. and Trivino, G. (2012). Selection of the best suitable sentences in linguistic descriptions of data. In Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., and Yager, R., editors, Advances in Computational Intelligence, volume 298 of Communications in Computer and Information Science, pages 295-304. Springer Berlin Heidelberg.
  18. Menendez-Gonzalez, C. and Trivino, G. (2011). Olap navigation in the granular linguistic model of a phenomenon. In Computational Intelligence and Data Mining (CIDM), 2011 IEEE Symposium on, pages 260-267.
  19. Ramos-Soto, A., Bugarín, A., and Barro, S. (2014a). Generación automática de predicciones a corto plazo: Metodología y validación. In Actas XVII Congreso Espan˜ol sobre Tecnologías y Lógica Fuzzy (ESTYLF), pages 405-410.
  20. Ramos-Soto, A., Bugarin, A., and Barro, S. (2014b). On the role of linguistic descriptions of data in the building of natural language generation systems. Submitted.
  21. Ramos-Soto, A., Bugarin, A., Barro, S., and Diaz-Hermida, F. (2013a). Automatic linguistic descriptions of meteorological data a soft computing approach for converting open data to open information. In Information Systems and Technologies (CISTI), 2013 8th Iberian Conference on, pages 1-6.
  22. Ramos-Soto, A., Bugarin, A., Barro, S., and Taboada, J. (2013b). Automatic generation of textual short-term weather forecasts on real prediction data. In Larsen, H., Martin-Bautista, M., Vila, M., Andreasen, T., and Christiansen, H., editors, Flexible Query Answering Systems, volume 8132 of Lecture Notes in Computer Science, pages 269-280. Springer Berlin Heidelberg.
  23. Ramos-Soto, A., Bugarin, A., Barro, S., and Taboada, J. (2014c). Linguistic descriptions for automatic generation of textual short-term weather forecasts on real prediction data. Fuzzy Systems, IEEE Transactions on, Early Access.
  24. Ramos-Soto, A., Díaz-Hermida, F., Barro, S., and Bugarín, A. (2012a). Validation of a linguistic summarization approach for time series meteorological data. In 5th ERCIM International Conference, page 133.
  25. Ramos-Soto, A., Díaz-Hermida, F., and Bugarín, A. (2012b). Construcción de resúmenes lingüísticos informativos sobre series de datos meteorológicos: informes climáticos de temperatura. In Actas XVI Congreso Espan˜ol sobre Tecnologías y Lógica Fuzzy (ESTYLF), pages 644-649.
  26. Reiter, E. and Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press.
  27. Sripada, S., Reiter, E., and Davy, I. (2003). Sumtimemousam: Configurable marine weather forecast generator. Expert Update, 6(3):4-10.
  28. van der Heide, A. and Trivino, G. (2009). Automatic generated linguistic summaries of energy consumption data. In Proceedings of 9th ISDA Conference, pages 553- 559.
  29. Wilbik, A. and Keller, J. M. (2012). A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems, 208:79 - 94.
  30. Yager, R. R. (1982). A new approach to the summarization of data. Information Sciences, 28(1):69 - 86.
  31. Yager, R. R., Ford, K. M., and Can˜as, A. J. (1990). An approach to the linguistic summarization of data. In Bouchon-Meunier, B., Yager, R. R., and Zadeh, L. A., editors, Uncertainty in Knowledge Bases, 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 90, Paris, France, July 2-6, 1990, Proceedings, volume 521 of Lecture Notes in Computer Science, pages 456-468. Springer.
  32. Zadeh, L. A. (1996). Fuzzy logic = computing with words. Fuzzy Systems, IEEE Transactions on, 4(2):103-111.
  33. Zadeh, L. A. (2000). From computing with numbers to computing with words : From manipulation of measurements to manipulation of perceptions. In Intelligent Systems and Soft Computing: Prospects, Tools and Applications, pages 3-40. Springer-Verlag.
  34. Zadeh, L. A. (2001). A new direction in ai - toward a computational theory of perceptions. In Reusch, B., editor, Computational Intelligence. Theory and Applications, volume 2206 of Lecture Notes in Computer Science, pages 628-628. Springer Berlin Heidelberg.
Download


Paper Citation


in Harvard Style

Ramos-Soto A., Bugarín A. and Barro S. (2015). Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data . In Doctoral Consortium - DCAART, (ICAART 2015) ISBN , pages 3-9


in Bibtex Style

@conference{dcaart15,
author={A. Ramos-Soto and A. Bugarín and S. Barro},
title={Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data},
booktitle={Doctoral Consortium - DCAART, (ICAART 2015)},
year={2015},
pages={3-9},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCAART, (ICAART 2015)
TI - Computing with Perceptions for the Linguistic Description of Complex Phenomena through the Analysis of Time Series Data
SN -
AU - Ramos-Soto A.
AU - Bugarín A.
AU - Barro S.
PY - 2015
SP - 3
EP - 9
DO -