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Multivariate time series classification is a machine learning task with increasing importance due to the proliferation of information sources in different domains (economy, health, energy, crops, etc.). Univariate methods lack the ability to capture the relationships between the different variables that compose a multivariate time series and therefore cannot be directly extrapolated to multivariate environments. Despite the good performance and competitive results of the multivariate proposals published to date, they are hard to interpret due to their high complexity. In this paper, we propose a multivariate time series classification method based on an alternative representation of the time series, composed of a set of 41 descriptive time series features, in order to improve the interpretability of time series and results obtained. Our proposal uses traditional classifiers over the extracted features to look for relationships between the different variables that form a multivariate time series. We have selected four state-of-the-art algorithms as base classifiers to evaluate our method. We have tested our proposal on the complete University of East Anglia repository, obtaining highly interpretable results capable of explaining the relationships between the features that compose the time series and achieving performance results statistically indistinguishable from the best algorithms of the state-of-the-art. [ABSTRACT FROM AUTHOR]

The classification of time series is a central problem in a wide range of disciplines. In this field, the state-of-the-art algorithm is COTE (Collective of Transformation-Based Ensembles) which is a combination of classifiers of different domains: time, autocorrelation, power spectrum and shapelets. The weakest point of this approach is its high computational burden which prevents its use in massive data environments. Shapelet Transform is one of the multiple algorithms that compose this ensemble. It has been shown to achieve a good performance over many reference datasets. Nevertheless, its computational complexity is also too high to be used in massive data environments. On the other hand, Big Data has emerged as an approach to manage massive datasets, which also applies to time series. We propose an algorithm for time series classification in a Big Data environment, DFST. It is based on a combination of the FastShapelet and Shapelet Transform ideas and it is the first completely scalable algorithm for time series classification. We have shown that our proposal scales linearly with the number of time series in dataset. In addition, the classification accuracy is equal to or higher than that of comparable sequential algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In time series predictor evaluation, we observe that with respect to the model selection procedure there is a gap between evaluation of traditional forecasting procedures, on the one hand, and evaluation of machine learning techniques on the other hand. In traditional forecasting, it is common practice to reserve a part from the end of each time series for testing, and to use the rest of the series for training. Thus it is not made full use of the data, but theoretical problems with respect to temporal evolutionary effects and dependencies within the data as well as practical problems regarding missing values are eliminated. On the other hand, when evaluating machine learning and other regression methods used for time series forecasting, often cross-validation is used for evaluation, paying little attention to the fact that those theoretical problems invalidate the fundamental assumptions of cross-validation. To close this gap and examine the consequences of different model selection procedures in practice, we have developed a rigorous and extensive empirical study. Six different model selection procedures, based on (i) cross-validation and (ii) evaluation using the series’ last part, are used to assess the performance of four machine learning and other regression techniques on synthetic and real-world time series. No practical consequences of the theoretical flaws were found during our study, but the use of cross-validation techniques led to a more robust model selection. To make use of the “best of both worlds”, we suggest that the use of a blocked form of cross-validation for time series evaluation became the standard procedure, thus using all available information and circumventing the theoretical problems. [Copyright &y& Elsevier]

Throughout the last years, optimization problems on a large number of variables, sometimes over 1000, are becoming common. Thus, algorithms that can tackle them effectively, both in result quality and run time, are necessary. Among these specific algorithms for high-dimensional problems, memetic algorithms, which are the result of the hybridization of an evolutionary algorithm and a local improvement technique, have arisen as very powerful optimization systems for this type of problems. A very effective algorithm of this kind is the MA-SW-Chains algorithm. On the other hand, general purpose computing using Graphics Processing Units (GPUs) has become a very active field because of the high speed-up ratios that can be obtained when applied to problems that exhibit a high degree of data parallelism. In this work we present a new design of MA-SW-Chains to adapt it to the GPU-based massively parallel architecture. The experiments with the new GPU memetic technique, compared to the original sequential version, prove that the results are obtained with the same quality but with a reduction of the run time of two orders of magnitude. This great improvement in computing time makes our proposal suitable for future optimization problems with a dimensionality several orders of magnitude greater than current high-dimensionality standards (i.e. problems with millions of variables). The remarkable run time reduction comes at the cost of a higher design and implementation overhead compared to the CPU-based single-threaded counterpart. [ABSTRACT FROM AUTHOR]

The k nearest neighbor ( k NN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. The use of graphics processing units (GPU) has improved the run-time performance of the k NN rule but the computational requirements of current approaches limit this performance as the dataset size increases. In this paper, we propose a new scalable and memory efficient design for a GPU-based k NN rule, called GPU-SME- k NN, that breaks the dependency between dataset size and memory footprint while delivering high performance. An experimental study of GPU-SME- k NN is presented showing a high performance, even in cases that other methods cannot address, while the computational requirements are suitable for most commercial GPU devices. Our design has also been applied to k NN-based lazy learning algorithms reducing run-times in a significant way. [ABSTRACT FROM AUTHOR]

Positive unlabeled time series classification has become an important area during the last decade, as often vast amounts of unlabeled time series data are available but obtaining the corresponding labels is difficult. In this situation, positive unlabeled learning is a suitable option to mitigate the lack of labeled examples. In particular, self-training is a widely used technique due to its simplicity and adaptability. Within this technique, the stopping criterion, i.e., the decision of when to stop labeling, is a critical part, especially in the positive unlabeled context. We propose a self-training method that follows the positive unlabeled approach for time series classification and a family of parameter-free stopping criteria for this method. Our proposal uses a graphical analysis, applied to the minimum distances obtained by the k -Nearest Neighbor as the base learner, to estimate the class boundary. The proposed method is evaluated in an experimental study involving various time series classification datasets. The results show that our method outperforms the transductive results obtained by previous models. [ABSTRACT FROM AUTHOR]

Fingerprint recognition has found a reliable application for verification or identification of people in biometrics. Globally, fingerprints can be viewed as valuable traits due to several perceptions observed by the experts; such as the distinctiveness and the permanence on humans and the performance in real applications. Among the main stages of fingerprint recognition, the automated matching phase has received much attention from the early years up to nowadays. This paper is devoted to review and categorize the vast number of fingerprint matching methods proposed in the specialized literature. In particular, we focus on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency. We identify the main properties and differences of existing methods. Then, we include an experimental evaluation involving the most representative local minutiae-based matching models in both verification and evaluation tasks. The results obtained will be discussed in detail, supporting the description of future directions. [ABSTRACT FROM AUTHOR]

Fingerprints are widely used in a variety of biometric identification systems. The fingerprint matching process is a processing step whose computational requirements limit the size of the fingerprint database that can be dealt with. Fingerprint matching algorithms based on minutiae are one of the most relevant families of biometric identification techniques. The scalability of these models is determined not only by the number of fingerprints but also the number of minutiae per fingerprint. Therefore, processing millions of fingerprints per second requires being able to process hundreds of millions of minutiae per second. In this paper we present a new design of the minutiae based fingerprint matching algorithm presented by Jiang et al. specifically created for GPU based massively parallel architectures. The parallel design allows speed-up ratios of up to 15 with one GPU compared to multi-threaded CPU implementations, and up to 54 using several GPUs in parallel and fingerprint processing rates of between 300,000 and 1,500,000 fingerprints per second. [ABSTRACT FROM AUTHOR]