Browsing Machine Learning by Title
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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 ... 
Multiobjectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions
(Evolutionary Computation, 201712)In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multiobjective optimisation, the algorithms from this field could be used for solving singleobjective problems as well. ... 
Natureinspired approaches for distance metric learning in multivariate time series classification
(IEEE Congress on Evolutionary Computation (CEC), 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 ... 
A note on the behavior of majority voting in multiclass domains with biased annotators
(IEEE Transactions on Knowledge and Data Engineering, 201805)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 ... 
A note on the Boltzmann distribution and the linear ordering problem
(Lecture Notes in Computer Science, 20161001)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 ... 
A novel adaptive densitybased ACO algorithm with minimal encoding redundancy for clustering problems
(2016 IEEE Congress on Evolutionary Computation, CEC 2016, 20161114)In the socalled Big Data paradigm descriptive analytics are widely conceived as techniques and models aimed at discovering knowledge within unlabeled datasets (e.g. patterns, similarities, etc) of utmost help for subsequent ... 
On the applicability of ant colony optimization to nonintrusive load monitoring in smart grids
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 20151231)Along with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying ... 
On the evaluation and selection of classifier learning algorithms with crowdsourced data
(Applied Soft Computing, 20190216)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. ... 
On the relevance of preprocessing in predictive maintenance for dynamic systems
(Predictive Maintenance in Dynamic Systems, 2018)The complexity involved in the process of realtime datadriven monitoring dynamic systems for predicted maintenance is usually huge. With more or less indepth any datadriven approach is sensitive to data preprocessing, ... 
OnLine Dynamic Time Warping for Streaming Time Series
(Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, 201709)Dynamic Time Warping is a wellknown measure of dissimilarity between time series. Due to its flexibility to deal with nonlinear distortions along the time axis, this measure has been widely utilized in machine learning ... 
An optimization tool to design the field of a Solar Power Tower plant allowing heliostats of different sizes
(International Journal of Energy Research, 20161030)The design of a Solar Power Tower plant involves the optimization of the heliostat field layout. Fields are usually designed to have all heliostats of identical size. Although the use of a single heliostat size has been ... 
Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints
(IEEE Transactions on Robotics, 20151231)Evolutionary algorithmbased unmanned aerial vehicle (UAV) path planners have been extensively studied for their effectiveness and flexibility. However, they still suffer from a drawback that the highquality waypoints in ... 
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutationbased 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 ... 
A review on distance based time series classification
(Data Mining and Knowledge Discovery,, 20181101)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 ... 
Soft range information for network localization
(IEEE Transactions on Signal Processing, 20180615)The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional rangebased localization approaches rely on ... 
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, ...