Abstract
Optimization studies often require very large computational resources to execute experiments. Furthermore, most of the time, the experiments are repetitions (same problem instances and same algorithm with the same parameters) that were carried out in past studies. In this work, we propose a framework for the execution of optimization experiments in a distributed environment and for the storage of the results as well as of the experimental conditions. The framework can support not only the organized execution of experiments but it also enables the reuse of the results in future studies.

Abstract
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of sentiment analysis in microblog streams. However, one of the main challenges consists in aggregating sentiment polarity in a timely fashion that can be fed to the prediction method. We investigated a large set of sentiment aggregate functions and performed a regression analysis using political opinion polls as gold standard. Our dataset contains nearly 233 000 tweets, classified according to their polarity (positive, negative or neutral), regarding the five main Portuguese political leaders during the Portuguese bailout (2011-2014). Results show that different sentiment aggregate functions exhibit different feature importance over time while the error keeps almost unchanged. © 2016 ACM.

Abstract
Several approaches have been applied to the task of algorithm selection. In this context, Meta-Learning provides an efficient solution by adopting a supervised strategy. Despite its promising results, Meta-Learning requires an adequate number of instances to produce a rich set of meta-examples. Recent approaches to generate synthetic or manipulated datasets have been adopted with success in the context of Meta-Learning. These proposals include the datasetoids approach, a simple data manipulation technique that generates new datasets from existing ones. Although such proposals can actually produce relevant datasets, they can eventually produce redundant, or even irrelevant, problem instances. Active Meta-Learning arises in this context to select only the most informative instances for meta-example generation. In this work, we investigate the Active Meta-Learning combined with datasetoids, focusing on using the Random forest algorithm in meta-learning. Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples and obtain a significant gain in Meta-Learning performance.

Abstract

Abstract

Abstract
One of the hardest resources to manage in retail is space. Retailers need to assign limited store space to a growing number of product categories such that sales and other performance metrics are maximized. Although this seems to be an ideal task for a data mining approach, there is one important barrier: the representativeness of the available data. In fact, changes to the layout of retail stores are infrequent. This means that very few values of the space variable are represented in the data, which makes it hard to generalize. In this paper, we describe a Decision Support System to assist retailers in this task. The system uses an Evolutionary Algorithm to optimize space allocation based on the estimated impact on sales caused by changes in the space assigned to product categories. We assess the quality of the system on a real case study, using different regression algorithms to generate the estimates. The system obtained very good results when compared with the recommendations made by the business experts. We also investigated the effect of the representativeness of the sample on the accuracy of the regression models. We selected a few product categories based on a heuristic assessment of their representativeness. The results indicate that the best regression models were obtained on products for which the sample was not the best. The reason for this unexpected results remains to be explained. © 2013 Springer-Verlag.