### Recent Submissions

• #### On the evaluation and selection of classifier learning algorithms with crowdsourced data ﻿

(Applied Soft Computing, 2019-02-16)
In many current problems, the actual class of the instances, the ground truth, is unavail- able. Instead, with the intention of learning a model, the labels can be crowdsourced by harvesting them from different annotators. ...
• #### K-means for massive data ﻿

(2019-04-30)
The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ...
• #### Early classification of time series using multi-objective optimization techniques ﻿

(Information Sciences, 2019-04-23)
In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...
• #### Mallows and generalized Mallows model for matchings ﻿

(Bernoulli, 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 ...
• #### perm mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems ﻿

(ACM Transactions on Mathematical Software, 2018)
Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of ...
• #### Aggregated outputs by linear models: An application on marine litter beaching prediction ﻿

(Information Sciences, 2019-01-01)
In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ...
• #### Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems ﻿

(Applied Soft Computing, 2017-02-28)
The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di- verse approaches. In this context this work focuses on the automatic con- ...
• #### On the relevance of preprocessing in predictive maintenance for dynamic systems ﻿

(Predictive Maintenance in Dynamic Systems, 2018)
The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, ...
• #### Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation ﻿

(Analytica Chimica Acta, 2018)
The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of ...
• #### Spatiotemporal information coupling in network navigation ﻿

(IEEE Transactions on Information Theory, 2018-12)
Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
• #### Crowd Learning with Candidate Labeling: an EM-based Solution ﻿

(Conference of the Spanish Association for Artificial Intelligence, 2018-09-27)
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 ...
• #### A review on distance based time series classification ﻿

(Data Mining and Knowledge Discovery,, 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 ...
• #### Detection of Sand Dunes on Mars Using a Regular Vine-based Classification Approach ﻿

(Knowledge- Based Systems, 2018-08)
This paper deals with the problem of detecting sand dunes from remotely sensed images of the surface of Mars. We build on previous approaches that propose methods to extract informative features for the classification of ...
• #### Distance-based exponential probability models on constrained combinatorial optimization problems ﻿

(GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, 2018-08-30)
Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained ...
• #### Bayesian inference for algorithm ranking analysis ﻿

(GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion 6 July 2018, Pages 324-325, 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 ...
• #### Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions ﻿

(Progress in Aerospace Sciences, 2018)
This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, ...
• #### A note on the behavior of majority voting in multi-class domains with biased annotators ﻿

(IEEE Transactions on Knowledge and Data Engineering, 2018-05)
Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...
• #### Effects of reducing VMs management times on elastic applications ﻿

(Journal of Grid Computing, 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 ...
• #### On-Line Dynamic Time Warping for Streaming Time Series ﻿

(Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, 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 ...
• #### Are the artificially generated instances uniform in terms of difficulty? ﻿

(IEEE Congress on Evolutionary Computation, 2018-06)
In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...