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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)
; 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 ...
Rank aggregation for non-stationary data streams
Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)
; Lobo, J.L.; Del Ser, J. (2022)
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 ...
On-line Elastic Similarity Measures for time series
Oregui, I.; Pérez, A.; Del Ser, J.; Lozano, J.A. (2019-04)
; Del Ser, J.; Lozano, J.A. (2019-04)
The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are ...
On-Line Dynamic Time Warping for Streaming Time Series
Oregui, I.; Pérez, A.; Del Ser, J.; Lozano, J.A. (2017-09)
; Del Ser, J.; Lozano, J.A. (2017-09)
Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning ...
Nature-inspired approaches for distance metric learning in multivariate time series classification
Oregui, I.; Del Ser, J.; Pérez, A.; Lozano, J.A. (2017-07)
; Pérez, A.; Lozano, J.A. (2017-07)
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 ...
Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems
Elola, A.; Del Ser, J.; Bilbao, M.; Perfecto, C.; Alexandre , E.; Salcedo-Sanz, S. (2017-02-28)
; Bilbao, M.; Perfecto, C.; Alexandre , E.; Salcedo-Sanz, S. (2017-02-28)
The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di- verse approaches. In this context this work focuses on the automatic con- ...
A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems
Villar-Rodriguez, E.; Gonzalez-Pardo, A.; Del Ser, J.; Bilbao, M.N.; Salcedo-Sanz, S. (2016-11-14)
; Bilbao, M.N.; Salcedo-Sanz, S. (2016-11-14)
In the so-called Big Data paradigm descriptive analytics are widely conceived as techniques and models aimed at discovering knowledge within unlabeled datasets (e.g. patterns, similarities, etc) of utmost help for subsequent ...
Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics
Del Ser, J.; Bilbao, M.N.; Perfecto, C.; Gonzalez-Pardo, A.; Campos-Cordobes, S. (2016-01-01)
; Bilbao, M.N.; Perfecto, C.; Gonzalez-Pardo, A.; Campos-Cordobes, S. (2016-01-01)
In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness ...
On the applicability of ant colony optimization to non-intrusive load monitoring in smart grids
Gonzalez-Pardo, A.; Del Ser, J.; Camacho, D. (2015-12-31)
; Camacho, D. (2015-12-31)
Along with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying ...