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Automated Qualitative Description of Measurements (1999)
| Venue: | In Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference |
| Citations: | 10 - 4 self |
BibTeX
@INPROCEEDINGS{Ruspini99automatedqualitative,
author = {Enrique H. Ruspini and Igor S. Zwir},
title = {Automated Qualitative Description of Measurements},
booktitle = {In Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference},
year = {1999},
pages = {1086--1091}
}
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Abstract
Measurements are usually thought of as precise numbers resulting from observation of a real-world system by means of special sensors or measuring devices. Humans frequently resort, however, to qualitative descriptions—based on the extent by which measurements agree with various models of relevance to the system being observed—to explain the nature and importance of a particular measurement or that of a set of related observations. When describing economic time series, for example, it is customary to point to major qualitative features of the series (e.g., uptrends, downtrends, oscillation patterns) rather than to sequences of precise values. In another important field of study — molecular biology—the positions of atoms in a large, complex molecule may be utilized to produce qualitative descriptions (e.g., shapes of significant molecular structures) that help in the understanding of molecular properties and function. We present results of ongoing research on methods for the automatic derivation of qualitative descriptions of complex objects. The ultimate goals of these investigations are the development of a methodology for the qualitative representation of complex objects, the systematic search and retrieval of measurements and objects based on those representations, and the discovery of knowledge based on the study of collections of such qualitative descriptions. Our techniques combine fuzzy logic and evolutionary computation methods to solve optimization problems associated with qualitative description. These methods are noteworthy in that they do not assume prior knowledge of the number of interesting structures, or their extension nor do they require an exhaustive explanation of the object being described. We present results of the application of these methods to the description of financial time series. ∗ On leave at Departmento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Spain. Currently visiting the Artificial
Keyphrases
qualitative description automated qualitative description complex object present result oscillation pattern financial time series real-world system major qualitative feature ultimate goal precise value study molecular biology evolutionary computation method qualitative representation complex molecule inteligencia artificial various model significant molecular structure precise number automatic derivation particular measurement optimization problem prior knowledge related observation fuzzy logic systematic search exhaustive explanation economic time series important field special sensor interesting structure molecular property
