Browsing Machine Learning by Title
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Early classification of time series by simultaneously optimizing the accuracy and earliness
(IEEE Transactions on Neural Networks and Learning Systems, 201710)The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to ... 
Early classification of time series using multiobjective optimization techniques
(Information Sciences, 20190423)In early classification of time series the objective is to build models which are able to make classpredictions for time series as accurately and as early as possible, when only a part of the series is available. It is ... 
Effects of reducing VMs management times on elastic applications
(Journal of Grid Computing, 201805)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 ... 
Efficient approximation of probability distributions with korder decomposable models
(International Journal of Approximate Reasoning, 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
(International Journal of Approximate Reasoning, 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
(KnowledgeBased Systems, 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 approximation to the Kmeans clustering for Massive Data
(KnowledgeBased Systems, 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 evolutionary algorithm for the orienteering problem
(Computers and Operations Research, 20170906)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 ... 
Estimating attraction basin sizes
(Lecture Notes in Computer Science, 20161001)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 ... 
Evolutionary algorithms to optimize lowthrust trajectory design in spacecraft orbital precession mission
(IEEE Congress on Evolutionary Computation, 20170606)In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the inplane rotation of space orbit, denominated as orbital precession. To overcome this ... 
Fitting the data from embryo implantation prediction: Learning from label proportions
(Statistical Methods in Medical Research, 20160101)Machine learning techniques have been previously used to assist clinicians to select embryos for humanassisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve ... 
Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems
(Applied Soft Computing, 20170228)The advent of the socalled 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 ... 
Improved Quantification of Important Beer Quality Parameters based on Nonlinear Calibration Methods applied to FTMIR Spectra
(Analytical and Bioanalytical Chemistry, 20160714)During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the ... 
An investigation of clustering strategies in manyobjective optimization: the IMulti algorithm as a case study
(Swarm Intelligence, 20170330)A variety of general strategies have been applied to enhance the performance of multiobjective optimization algorithms for manyobjective optimization problems (those with more than three objectives). One of these strategies ... 
Joint topology optimization, power control and spectrum allocation for intravehicular multihop sensor networks using dandelionencoded heuristics
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 20160101)In the last years the interest in multihop communications has gained momentum within the research community due to the challenging characteristics of the intravehicular radio environment and the stringent robustness ... 
Kmeans for massive data
(20190430)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 ... 
Learning to classify software defects from crowds: a novel approach
(Applied Soft Computing, 20171101)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 ... 
Mallows and generalized Mallows model for matchings
(Bernoulli, 20190225)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 ... 
Measuring the Classimbalance Extent of Multiclass Problems
(Pattern Recognition Letter, 20170730)Since many important realworld classification problems involve learning from unbalanced data, the challenging classimbalance problem has lately received con siderable attention in the community. Most of the methodological ... 
Multiobjective Optimization of Orbit Transfer Trajectory Using Imperialist Competitive Algorithm
(IEEE Aerospace Conference, 20170304)This paper proposes a systematic direct approach to carry out effective multiobjective optimization of space orbit transfer with highlevel thrust acceleration. The objective is to provide a transfer trajectory with ...