George G. Cabral

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One-class classification is an important problem with applications in several different areas such as novelty detection, outlier detection and machine monitoring. In this paper we propose a novel method for one-class classification, referred to as NNDDSRM. It is based on the principle of structural risk minimization and the nearest neighbor data description(More)
This paper introduces a novel instance-based one-class classification method for novelty detection in time series based on its states transition. The main feature of our work is to generate an efficient method which automatically finds the parameters (whose yields the best model) according with the quality of the discovered time series states and the(More)
Machine learning techniques are an important tool for diagnosing a number of diseases, as has been shown by the recent literature. Hospitals and medical clinics have a huge amount of data about the treatment of their patients, however, rarely analysis of these data is performed in order to extract intrinsic information aimed at modeling a specific problem.(More)