Browsing by Author "Perez, Aritz"

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    Adversarial sample crafting for time series classification with elastic similarity measures
    (Springer Verlag, 2018) Oregi, Izaskun; Del Ser, Javier; Perez, Aritz; Lozano, Jose A.; Quantum
    Adversarial Machine Learning (AML) refers to the study of the robustness of classification models when processing data samples that have been intelligently manipulated to confuse them. Procedures aimed at furnishing such confusing samples exploit concrete vulnerabilities of the learning algorithm of the model at hand, by which perturbations can make a given data instance to be misclassified. In this context, the literature has so far gravitated on different AML strategies to modify data instances for diverse learning algorithms, in most cases for image classification. This work builds upon this background literature to address AML for distance based time series classifiers (e.g., nearest neighbors), in which attacks (i.e. modifications of the samples to be classified by the model) must be intelligently devised by taking into account the measure of similarity used to compare time series. In particular, we propose different attack strategies relying on guided perturbations of the input time series based on gradient information provided by a smoothed version of the distance based model to be attacked. Furthermore, we formulate the AML sample crafting process as an optimization problem driven by the Pareto trade-off between (1) a measure of distortion of the input sample with respect to its original version; and (2) the probability of the crafted sample to confuse the model. In this case, this formulated problem is efficiently tackled by using multi-objective heuristic solvers. Several experiments are discussed so as to assess whether the crafted adversarial time series succeed when confusing the distance based model under target.
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    Nature-inspired approaches for distance metric learning in multivariate time series classification
    (Institute of Electrical and Electronics Engineers Inc., 2017-07-05) Oregi, Izaskun; Del Ser, Javier; Perez, Aritz; Lozano, Jose A.; Quantum; IA
    The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular class of data. In this context the related literature has extensively shown that dynamic time warping is the similarity measure of choice when univariate time series are considered. However, possible statistical coupling among different dimensions make the generalization of this metric to the multivariate case all but obvious. This has ignited the interest of the community in new distance definitions capable of capturing such inter-dimension dependences. In this paper we propose a simple dynamic time warping based distance that finds the best weighted combination between the dependent - where multivariate time series are treated as whole - and independent approaches - where multivariate time series are just a collection of unrelated univariate time series - of the time series to be classified. A benchmark of four heuristic wrappers, namely, simulated annealing, particle swarm optimization, estimation of distribution algorithms and genetic algorithms are used to evolve the set of weighting coefficients towards maximizing the cross-validated predictive score of the classifiers. In this context one of the most recurring classifiers is nearest neighbor. This classifier is couple with a distance that as afore mentioned, in most cases, have been dynamic time warping. The performance of the proposed approach is validated over datasets widely utilized in the related literature, from which it is concluded that the obtained performance gains can be enlarged by properly decoupling the influence of each dimension in the definition of the dependent dynamic time warping distance.
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    Rank Aggregation for Non-stationary Data Streams
    (Springer, 2021-09-11) Irurozki, Ekhine; Perez, Aritz; Lobo, Jesus; Del Ser, Javier; Oliver, Nuria; Pérez-Cruz, Fernando; Kramer, Stefan; Read, Jesse; Lozano, Jose A.; IA
    The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. As a main result, we bound the minimum number of samples required to output the ground truth with high probability. Besides, we show how the optimal parameters are set. Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more reliable than others. We show that, under mild assumptions, this generalization can solve the problem of rank aggregation over non-stationary data streams.