Browsing Machine Learning by Title
Now showing items 1-20 of 122
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An active adaptation strategy for streaming time series classification based on elastic similarity measures
Oregi, I.; Pérez, A.
; Del Ser, J.; Lozano, J.A. (2022-05-21)
In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line ... -
Ad-Hoc Explanation for Time Series Classification
Abanda, A.; Mori, U.; Lozano, J.A. (2022)
In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ... -
An adaptive neuroevolution-based hyperheuristic
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2020)
According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic ... -
Advances on Time Series Analysis using Elastic Measures of Similarity
(2020-07-23)A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series ... -
Aggregated outputs by linear models: An application on marine litter beaching prediction
Hernández-González, J.; Inza, I.; Granado, I.; Basurko, O.C.; Fernández, J.A.; Lozano, J.A. (2019-01-01)
In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ... -
Algorithms for Large Orienteering Problems
Kobeaga, G. (2021-01)
In this thesis, we have developed algorithms to solve large-scale Orienteering Problems. The Orienteering Problem is a combinatorial optimization problem were given a weighted complete graph with vertex profits and a maximum ... -
Alternative Representations for Codifying Solutions in Permutation-Based Problems
(2020-07-01)Since their introduction, Estimation of Distribution Algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutation-based ... -
An empirical study on collective intelligence algorithms for video games problem-solving
(2015-12-31)Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or ... -
Analysis of a hybrid Genetic Simulated Annealing strategy applied in multi-objective optimization of orbital maneuvers
Shirazi, A. (2017-03-27)
Optimization of orbital maneuvers is one of the main issues in conceptual and preliminary design of spacecraft in different space missions. The main issue in optimization of high-thrust orbit transfers is that the common ... -
Analysis of Dominant Classes in Universal Adversarial Perturbations
Vadillo, J.; Santana, R.; Lozano, J.A. (2022)
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ- ent strategies can be employed to efficiently generate adversarial attacks, some ... -
Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process.
Montenegro, C.; Santana, R.; Lozano, J.A. (2021)
An End-Of-Turn Detection Module (EOTD-M) is an essential component of au- tomatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting ... -
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
Carreño, A.; Inza, I.; Lozano, J.A. (2019-09-01)
In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ... -
Anatomy of the attraction basins: Breaking with the intuition
Hernando, L.; Mendiburu, A.; Lozano, J.A. (2019)
olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ... -
Application of machine learning techniques to weather forecasting
(2018-10-24)Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the ... -
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2019-07)
The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ... -
Are the artificially generated instances uniform in terms of difficulty?
Pérez, A.; Ceberio, J.; Lozano, J.A. (2018-06)
In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ... -
Bayesian inference for algorithm ranking analysis
Calvo, B.; Ceberio, J.; Lozano, J.A. (2018-08-30)
The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ... -
Bayesian Optimization Approaches for Massively Multi-modal Problems
Roman, I.; Mendiburu, A.; Santana, R.; Lozano, J.A. (2019)
The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ... -
Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization
Mehryary, S.; Mazuelas, S.; Malekzadehz, P.; Spachos, P.; Plataniotisy, K.N.; Mohammadi, A. (2019)
Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ... -
Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation
(2018)The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of ...