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Jose Antonio Lozano Alonso

Scientific Director

T +34 946 567 842
F +34 946 567 842
E jlozano@bcamath.org

Information of interest

My research interests are in the field of Statistical Machine Learning and Combinatorial Optimization. Particularly in Machine Learning we pursue the design and evaluation of new classification paradigms and algorithms able to produce predictive models which can be applied in different fields such as medicine, bioinformatics, ecology, etc. On the other hand, in the Combinatorial Optimization field we develop new heuristics and metaheuristic algorithms able to find a balance between quality of the solution and computational time, study their properties from a theoretical point of view and apply in the solution of real problems.

  • Fast K-Medoids With the l_1-Norm 

    Capó, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2023-07-26)
    K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of ...
  • On the Use of Second Order Neighbors to Escape from Local Optima 

    Torralbo, M.; Hernando, L.; Contreras, E.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2023-07-12)
    Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and effi- cient strategy, the Extended Reach, that escapes from local optima ob- tained ...
  • The Natural Bias of Artificial Instances 

    Unanue, I.; Merino, M.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2023)
    Many exact and metaheuristic algorithms presented in the literature are tested by comparing their performance in different sets of instances. However, it is known that when these sets of instances are generated randomly, ...
  • A mathematical analysis of EDAs with distance-based exponential models 

    Unanue, I.; Merino, M.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2022-09-01)
    Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying ...
  • Time Series Classifier Recommendation by a Meta-Learning Approach 

    Abanda, A.Autoridad BCAM; Mori, U.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2022-03-26)
    This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set ...
  • Ad-Hoc Explanation for Time Series Classification 

    Abanda, A.Autoridad BCAM; Mori, U.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2022)
    In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ...
  • Analysis of Dominant Classes in Universal Adversarial Perturbations 

    Vadillo, J.; Santana, R.; Lozano, J.A.Autoridad BCAM (2022)
    The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ- ent strategies can be employed to efficiently generate adversarial attacks, some ...
  • LASSO for streaming data with adaptative filtering 

    Capó, M.; Pérez, A.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2022)
    Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic of current interest. On the other hand, the problem of l1-penalized ...
  • 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, ...
  • 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 ...
  • A Review on Outlier/Anomaly Detection in Time Series Data 

    Blázquez-García, A.; Conde, A.; Mori, U.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2021)
    Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for ...
  • Water leak detection using self-supervised time series classification 

    Blázquez-García, A.; Conde, A.; Mori, U.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2021)
    Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails ...
  • Journey to the center of the linear ordering problem 

    Hernando, L.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (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.Autoridad BCAM; Mazuelas, S.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (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.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (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 ...
  • in-depth analysis of SVM kernel learning and its components 

    Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2020)
    The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ...
  • A mathematical analysis of edas with distance-based exponential models 

    Unanue, I.; Merino, M.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2019-07-01)
    Estimation of Distribution Algorithms have been successfully used for solving many combinatorial optimization problems. One type of problems in which Estimation of Distribution Algorithms have presented strong competitive ...
  • On-line Elastic Similarity Measures for time series 

    Oregui, I.; Pérez, A.Autoridad BCAM; Del Ser, J.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (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 ...
  • Mallows and generalized Mallows model for matchings 

    Irurozki, E.; Calvo, B.; Lozano, J.A.Autoridad BCAM (2019-02-25)
    The Mallows and Generalized Mallows Models are two of the most popular probability models for distribu- tions on permutations. In this paper, we consider both models under the Hamming distance. This models can be seen as ...
  • Sentiment analysis with genetically evolved Gaussian kernels 

    Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2019)
    Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...
  • Anatomy of the attraction basins: Breaking with the intuition 

    Hernando, L.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2019)
    olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ...
  • Hybrid Heuristics for the Linear Ordering Problem 

    Garcia, E.; Ceberio, J.; Lozano, J.A.Autoridad BCAM (2019)
    The linear ordering problem (LOP) is one of the classical NP-Hard combinatorial optimization problems. Motivated by the difficulty of solving it up to optimality, in recent decades a great number of heuristic and meta-heuristic ...
  • Bayesian Optimization Approaches for Massively Multi-modal Problems 

    Roman, I.; Mendiburu, A.; Santana, R.; Lozano, J.A.Autoridad BCAM (2019)
    The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ...
  • A review on distance based time series classification 

    Abanda, A.Autoridad BCAM; Mori, U.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2018-11-01)
    Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ...
  • Bayesian inference for algorithm ranking analysis 

    Calvo, B.; Ceberio, J.; Lozano, J.A.Autoridad BCAM (2018-08-30)
    The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ...
  • Effects of reducing VMs management times on elastic applications 

    Pascual, J.A.; Lozano, J.A.Autoridad BCAM; Miguel-Alonso, J. (2018-05)
    Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers ...
  • Learning to classify software defects from crowds: a novel approach 

    Hernández-González, J.; Rodríguez, D.; Inza, I.; Rachel, H.; Lozano, J.A.Autoridad BCAM (2017-11-01)
    In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...
  • An efficient evolutionary algorithm for the orienteering problem 

    Kobeaga, G.Autoridad BCAM; Merino, M.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2017-09-06)
    This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem, each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject ...
  • On-Line Dynamic Time Warping for Streaming Time Series 

    Oregui, I.; Pérez, A.Autoridad BCAM; Del Ser, J.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (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 ...
  • Measuring the Class-imbalance Extent of Multi-class Problems 

    Ortigosa-Hernández, J.; Inza, I.; Lozano, J.A.Autoridad BCAM (2017-07-30)
    Since many important real-world classification problems involve learning from unbalanced data, the challenging class-imbalance problem has lately received con- siderable attention in the community. Most of the methodological ...
  • Estimating attraction basin sizes 

    Hernando, L.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2016-10-01)
    The performance of local search algorithms is influenced by the properties that the neighborhood imposes on the search space. Among these properties, the number of local optima has been traditionally considered as a ...
  • A note on the Boltzmann distribution and the linear ordering problem 

    Ceberio, J.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2016-10-01)
    The Boltzmann distribution plays a key role in the field of optimization as it directly connects this field with that of probability. Basically, given a function to optimize, the Boltzmann distribution associated to this ...

More information

On-line Elastic Similarity Measures

Adaptation of the most frequantly used elastic similarity measures: Dynamic Time Warping (DTW), Edit Distance (Edit), Edit Distance for Real Sequences (EDR) and Edit Distance with Real Penalty (ERP) to on-line setting.

Authors: Izaskun Oregi, Aritz Perez, Javier Del Ser, Jose A. Lozano

License: free and open source software

OPLib

OPLib: Test instances for the Orienteering Problem

Authors: Gorka Kobeaga, Maria Merino, Jose A. Lozano

License: free and open source software

RB&C and EA4OP

In this repository, you will find the implementation of two algorithms to solve the Orienteering Problem (OP): RB&C (exact) https://doi.org/10.1016/j.ejor.2023.07.034 and EA4OP (heuristic) https://doi.org/10.1016/j.cor.2017.09.003.

Authors: Gorka Kobeaga, Maria Merino, Jose A. Lozano

License: free and open source software

A307429 sequence

OEIS sequence with the number of permutations of {1..n} at Kendall tau distance k of permutation sigma1 and k+1 Kendall tau distance of permutation sigma2, where sigma1 and sigma2 are at Kendall tau distance 1. Published in https://doi.org/10.1007/s12293-022-00371-y

Authors: Imanol Unanue, Maria Merino, Jose A. Lozano

License: free and open source software

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