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Data stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear or evolve over time. Most of the works found in the ND literature presents it as a binary classification task. In several data stream real life problems, ND must be treated as a multiclass task, in which, the known concept is composed by one or more classes and different new classes may appear. This work proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a multiclass task. In the initial training phase, MINAS builds a decision model based on a labeled data set. In the online phase, new examples are classified using this model, or marked as unknown. Groups of unknown examples can be used later to create valid novelty patterns (NP), which are added to the current model. The decision model is updated as new data come over the stream in order to reflect changes in the known Responsible editor: Charu Aggarwal.

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Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Among the methods used, Artificial Neural Networks have been particularly successful and have been incorporated into several computational tools. However, the design of efficient Artificial Neural Networks is largely affected by the definition of adequate values for their free parameters. This article discusses a new approach to the design of a particular Artificial Neural Networks model, RBF networks, through Genetic Algorithms. It presents an overall view of the problems involved and the different approaches employed to optimize Artificial Neural Networks genetically. For such, several methods proposed in the literature for optimizing RBF networks using Genetic Algorithms are discussed. Finally, the model proposed by the authors is described and experimental results using this model for a credit risk assessment problem are presented.

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A fast procedure using time-domain nuclear magnetic resonance (TD-NMR) to detect olive oil adulteration with polyunsaturated vegetable oils in filled bottles is proposed. The 1 H transverse relaxation times (T 2 ) of 37 commercial samples were measured using low-field nuclear magnetic resonance (LF-NMR) spectrometer and a unilateral nuclear magnetic resonance (UNMR) sensor. Results obtained with LF-NMR revealed better feasibility when compared with the UNMR sensor, with higher signal-to-noise (S/N) ratio and larger difference in the T 2 decays. Principal component analysis (PCA) exhibited tight and well-separated clusters of pure olive oil (OO), pure soybean oil (SO), and blends of OO/SO (adulterated samples). Soft independent modeling of class analogies analysis (SIMCA) classification model indicated that five brands of olive oil commercialized in Brazil were adulterated with polyunsaturated fatty acids, further confirmed by high-resolution NMR. Overall, LF-NMR provided a fast procedure for screening olive oil authenticity directly in the sealed bottles.