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Implementing the Cumulative Difference Plot in the IOHanalyzer 

Arza, E.Autoridad BCAM; Ceberio, J.; Irurozki, E.; Pérez, A.Autoridad BCAM (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 ...
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Generalized Maximum Entropy for Supervised Classification 

Mazuelas, S.Autoridad BCAM; Shen, Y.; Pérez, A.Autoridad BCAM (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 ...
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Rank aggregation for non-stationary data streams 

Irurozki, E.; Pérez, A.Autoridad BCAM; Lobo, J.L.; Del Ser, J.Autoridad BCAM (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 ...
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Statistical assessment of experimental results: a graphical approach for comparing algorithms 

Arza, E.Autoridad BCAM; Ceberio, J.; Irurozki, E.; Pérez, A.Autoridad BCAM (2021-08-25)
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples ...
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A cheap feature selection approach for the K -means algorithm 

Capo, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2021-05)
The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision or sensor networks, represents a challenge for the K-means algorithm. In this regard, ...
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A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients 

Rakshit, P.; Zaballa-Larumbe, O.; Pérez, A.Autoridad BCAM; Gomez-Inhiesto, E.; Acaiturri-Ayesta, M.T.; Lozano, J.A.Autoridad BCAM (2021)
This paper presents a novel machine learning approach to per- form an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: i) in ...
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On the fair comparison of optimization algorithms in different machines 

Arza, E.Autoridad BCAM; Pérez, A.Autoridad BCAM; Ceberio, J.; Irurozki, E. (2021)
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to ...
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K-means for Evolving Data Streams 

Bidaurrazaga, A.Autoridad BCAM; Pérez, A.Autoridad BCAM; Capó, M. (2021-01-01)
Nowadays, streaming data analysis has become a relevant area of research in machine learning. Most of the data streams available are unlabeled, and thus it is necessary to develop specific clustering techniques that take ...
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Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer. 

Zaballa, O.Autoridad BCAM; Pérez, A.Autoridad BCAM; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A.Autoridad BCAM (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 ...
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Minimax Classification with 0-1 Loss and Performance Guarantees 

Mazuelas, S.Autoridad BCAM; Zanoni, A.; Pérez, A.Autoridad BCAM (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 ...
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Pérez, A. (28)
Lozano, J.A. (13)Ceberio, J. (7)Irurozki, E. (7)Arza, E. (6)Del Ser, J. (5)Capo, M. (4)Oregui, I. (4)Calvo, B. (3)Mazuelas, S. (3)... másSubjectK-means (3)K-means++ (3)Supervised classification (3)supervised classification (3)Approximating probability distributions (2)clustering (2)minibatch K-means (2)Time series (2)Algorithms (1)Benchmarking (1)... másFecha2022 (3)2021 (5)2020 (7)2019 (4)2018 (2)2017 (4)2016 (3)

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