Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

Knowledge Extraction for Question Titling.

This article describes the work performed over the database of questions belonging to the different opinion polls carried during the last 50 years in Spain. Approximately half of the questions are provided with a title while the other half remain untitled. The work and implemented techniques in order to automatically generate the titles for untitled questions are described. This process is performed over very short texts and generated titles are subject to strong stylistic conventions and should be fully grammatical pieces of Spanish

Evidence of the presence of bias in subjective metrics: analysis within a family of experiments

Context: Measurement is crucial and important to empirical software engineering. Although reliability and validity are two important properties warranting consideration in measurement processes, they may be influenced by random or systematic error (bias) depending on which metric is used. Aim: Check whether, the simple subjective metrics used in empirical software engineering studies are prone to bias. Method: Comparison of the reliability of a family of empirical studies on requirements elicitation that explore the same phenomenon using different design types and objective and subjective metrics. Results: The objectively measured variables (experience and knowledge) tend to achieve more reliable results, whereas subjective metrics using Likert scales (expertise and familiarity) tend to be influenced by systematic error or bias. Conclusions: Studies that predominantly use variables measured subjectively, like opinion polls or expert opinion acquisition.