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Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.
Zaballa, O.
; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2020-12-29)
; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2020-12-29)
The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us ...
Minimax Classification with 0-1 Loss and Performance Guarantees
Mazuelas, S.; Zanoni, A.; Pérez, A. (2020-12-01)
; Zanoni, A.; Pérez, A. (2020-12-01)
Supervised classification techniques use training samples to find classification
rules with small expected 0-1 loss. Conventional methods achieve efficient learning
and out-of-sample generalization by minimizing surrogate ...
Statistical model for reproducibility in ranking-based feature selection
Urkullu, A.; Pérez, A.; Calvo, B. (2020-11-05)
; Calvo, B. (2020-11-05)
The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the ...
General supervision via probabilistic transformations
Mazuelas, S.; Pérez, A. (2020-08-01)
; Pérez, A. (2020-08-01)
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
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 ...
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Arza, E.; Pérez, A.; Irurozki, E.; Ceberio, J. (2020-07)
; Pérez, A.; Irurozki, E.; Ceberio, J. (2020-07)
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard ...
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 ...
Journey to the center of the linear ordering problem
Hernando, L.; Mendiburu, A.; Lozano, J.A. (2020-06)
(2020-06)
A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the ...
Probabilistic Load Forecasting Based on Adaptive Online Learning
Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2020)
; Mazuelas, S.; Lozano, J.A. (2020)
Load forecasting is crucial for multiple energy management
tasks such as scheduling generation capacity, planning
supply and demand, and minimizing energy trade costs. Such
relevance has increased even more in recent ...
An efficient K-means clustering algorithm for tall data
Capo, M.; Pérez, A.; Lozano, J.A. (2020)
; Lozano, J.A. (2020)
The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial ...