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Implementing the Cumulative Difference Plot in the IOHanalyzer
Arza, E.
; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ...
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 ...
Generalized Maximum Entropy for Supervised Classification
Mazuelas, S.; Shen, Y.; Pérez, A. (2022-04)
; Shen, Y.; Pérez, A. (2022-04)
The maximum entropy principle advocates to
evaluate events’ probabilities using a distribution that maximizes
entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
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 ...