Abstract
This paper describes a methodology to remove the background of the lanes in Thin Layer Chromatography (TLC) images, aiming at improving band detection and classification. The storage of the biological samples to be analyzed by TLC is usually done via plastic containers. Filter paper is an alternative that allows reduced costs and higher portability, but it increases the complexity of the image analysis stage due to lane background alteration. In order to overcome this problem, a negative control lane is included in every chromatographic plate. After image preprocessing and lane detection stages, a background profile is generated by processing the negative control lane using the Discrete Wavelet Transform (DWT). This profile is then subtracted to the profiles of all other sample lanes in order to overcome the data degradation introduced by filter paper usage. For assessing the proposed background removal process, 105 TLC lanes, with and without background, were used as input for three one-class classifiers. In all cases, the best results were achieved for the lanes after background removal.

Abstract
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Abstract
Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once. Copyright 2013 ACM.

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Abstract
In this work, we propose a general integer programming model to address the staff scheduling problem, flexible enough to be easily adapted to a wide-range of real-world problems. The model is applied with slight changes to two case studies: a glass plant and a continuous care unit, and also to a collection of benchmark instances available in the literature. The emphasis of our approach is on a novel formulation of sequence constraints and also on workload balance, which is tackled through cyclic scheduling. Models are solved using the CPLEX solver. Computational results indicate that optimal solutions can be achieved within a reasonable amount of time.

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.