CESE - Indexed Articles in Journals

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Browsing CESE - Indexed Articles in Journals by Author "Alípio Jorge"
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    A data warehouse to support web site automation
    ( 2014) Domingues,MA ; Carlos Manuel Soares ; Alípio Jorge ; Rezende,SO
    Background: Due to the constant demand for new information and timely updates of services and content in order to satisfy the user’s needs, web site automation has emerged as a solution to automate several personalization and management activities of a web site. One goal of automation is the reduction of the editor’s effort and consequently of the costs for the owner. The other goal is that the site can more timely adapt to the behavior of the user, improving the browsing experience and helping the user in achieving his/her own goals. Methods: A database to store rich web data is an essential component for web site automation. In this paper, we propose a data warehouse that is developed to be a repository of information to support different web site automation and monitoring activities. We implemented our data warehouse and used it as a repository of information in three different case studies related to the areas of e-commerce, e-learning, and e-news. Result: The case studies showed that our data warehouse is appropriate for web site automation in different contexts. Conclusion: In all cases, the use of the data warehouse was quite simple and with a good response time, mainly because of the simplicity of its structure. © 2014, Domingues et al.; licensee Springer.
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    Discovering a taste for the unusual: exceptional models for preference mining
    ( 2018) de Sa,CR ; Knobbe,A ; Carlos Manuel Soares ; Alípio Jorge ; Paulo Jorge Azevedo ; Duivesteijn,W ; 4981 ; 5606 ; 5001
    Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge. © 2018 The Author(s)
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    Ensemble approaches for regression: A survey
    ( 2012) Jorge Freire de Sousa ; João Mendes Moreira ; Carlos Manuel Soares ; Alípio Jorge
    The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.
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    Improving the accuracy of long-term travel time prediction using heterogeneous ensembles
    ( 2015) João Mendes Moreira ; Alípio Jorge ; Jorge Freire Sousa ; Carlos Manuel Soares
    This paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners.
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    Preference Rules for Label Ranking: Mining Patterns in Multi-Target Relations
    ( 2017) Cláudio Rebelo Sá ; Paulo Jorge Azevedo ; Carlos Manuel Soares ; Alípio Jorge ; Knobbe,A