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Generating automatic linguistic descriptions with big data
ایجاد توصیفات زبانی خودکار با داده های بزرگ-2017 In highly connected world, the volume and variety of data is growing and growing. The
Big Data era opens new challenges to address. Dealing with Big Data, we have identified
and analyzed seven issues: (1) scalability, (2) efficient processing, (3) incomplete and in
accurate data, (4) specific domains, (5) relevance of information, (6) levels of detail, and
(7) intuitive and effective knowledge representation. The analysis reveals that five of these
issues are related to knowledge representation and human perception. Linguistic Descrip
tions of Complex Phenomena is a technology aimed to compute and generate linguistic
reports customized to the user needs. In this paper, we present and describe an approach
to Big Data based on this technology that faces the seven issues under study. Namely, we
generate linguistic reports from Big Data that fulfill with the user requirements. To eval
uate the generated linguistic reports we propose specific evaluation criteria based on the
maxims of Grice. We illustrate the usefulness of the proposed solution by presenting a
practical experiment based on the census data of the United States of America.
Keywords:Linguistic descriptions|Big data|MapReduce|Fuzzy logic |
مقاله انگلیسی |
2 |
Fuzzy rule base ensemble generated from data by linguistic associations mining
گروه مبتنی بر قوانین فازی تولید شده از داده ها توسط کاوش انجمنی زبانی-2015 As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques, that combine several individual forecasts, are being proposed. In this contribution, we employ the so-called Fuzzy Rule-Based Ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases based on time series features such as trend, seasonality, or stationarity. For identification of fuzzy rule bases, we use the linguistic association mining. A huge experimental justification is provided.
Keywords: Fuzzy rule-based ensemble | Time series | Fuzzy IF–THEN rules | Linguistic description | Perception-based logical deduction |
مقاله انگلیسی |