Browsing by Author "Pérez, A."
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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. (20220521)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 online ... 
An adaptive neuroevolutionbased hyperheuristic
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2020)According to the NoFreeLunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problemspecific knowledge usually outperform more generic ... 
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (201907)The Quadratic Assignment Problem (QAP) is a specially challenging permutationbased nphard 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. (201806)In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a testbed to determine the performance of the algorithms at hand. In this context, a recent work on ... 
A cheap feature selection approach for the K means algorithm
Capo, M.; Pérez, A.; Lozano, J.A. (202105)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 Kmeans algorithm. In this regard, ... 
Crowd Learning with Candidate Labeling: an EMbased Solution
BeñaranMuñoz, I.; HernándezGonzález, J.; Pérez, A. (20180927)Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ... 
Efficient approximation of probability distributions with korder decomposable models
Pérez, A.; Inza, I.; Lozano, J.A. (20160101)During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ... 
Efficient approximation of probability distributions with korder decomposable models
Pérez, A.; Inza, I.; Lozano, J.A. (201607)During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ... 
An efficient approximation to the Kmeans clustering for Massive Data
Capo, M.; Pérez, A.; Lozano, J.A. (20170201)Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ... 
An efficient approximation to the Kmeans clustering for Massive Data
Capo, M.; Pérez, A.; Lozano, J.A. (20160628)Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ... 
An efficient Kmeans clustering algorithm for tall data
Capo, M.; Pérez, A.; 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 ... 
General supervision via probabilistic transformations
Mazuelas, S.; Pérez, A. (20200801)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 ... 
Generalized Maximum Entropy for Supervised Classification
Mazuelas, S.; Shen, Y.; Pérez, A. (202204)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 ... 
Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.
Zaballa, O.; Pérez, A.; GómezInhiesto, E.; AcaiturriAyesta, T.; Lozano, J.A. (20201229)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 ... 
Implementing the Cumulative Difference Plot in the IOHanalyzer
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (202207)The IOHanalyzer is a webbased 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 ... 
Kmeans for Evolving Data Streams
Bidaurrazaga, A.; Pérez, A.; Capó, M. (20210101)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 ... 
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Arza, E.; Pérez, A.; Irurozki, E.; Ceberio, J. (202007)The Quadratic Assignment Problem (QAP) is a wellknown permutationbased combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NPhard ... 
A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients
Rakshit, P.; ZaballaLarumbe, O.; Pérez, A.; GomezInhiesto, E.; AcaiturriAyesta, M.T.; Lozano, J.A. (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 ... 
Minimax Classification with 01 Loss and Performance Guarantees
Mazuelas, S.; Zanoni, A.; Pérez, A. (20201201)Supervised classification techniques use training samples to find classification rules with small expected 01 loss. Conventional methods achieve efficient learning and outofsample generalization by minimizing surrogate ... 
Natureinspired approaches for distance metric learning in multivariate time series classification
Oregui, I.; Del Ser, J.; Pérez, A.; Lozano, J.A. (201707)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 ...