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Time series classification is an important task in data mining that has been traditionally addressed with the use of similarity-based classifiers. The 1-NN DTW is typically considered the most accurate model for temporal data. Nevertheless, some authors have recently proposed ingenious alternatives to the 1-NN DTW by using diversity of time series representation or by using DTW for feature extraction...
While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time series mean that the combination of nearest neighbor classifiers and Dynamic Time Warping (DTW) is very competitive across a host of domains, from medicine to astronomy to environmental sensors. While there has been significant progress in improving the...
Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical...
Most Machine Learning systems target into inducing classifiers with optimal coverage and precision measures. Although this constitutes a good approach for prediction, it might not provide good results when the user is more interested in description. In this case, the induced models should present other properties such as novelty, interestingness and so forth. In this paper we present a research work...
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