Browsing Machine Learning by Issue Date
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Natureinspired approaches for distance metric learning in multivariate time series classification
(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 ... 
Measuring the Classimbalance Extent of Multiclass Problems
(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 ... 
The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches
(20170830)This work deals with the socalled weighted independent domination problem, which is an $NP$hard combinatorial optimization problem in graphs. In contrast to previous work, this paper considers the problem from a ... 
OnLine Dynamic Time Warping for Streaming Time Series
(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 efficient evolutionary algorithm for the orienteering problem
(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 ... 
Early classification of time series by simultaneously optimizing the accuracy and earliness
(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 ... 
Learning to classify software defects from crowds: a novel approach
(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 ... 
A system for airport weather forecasting based on circular regression trees
(20171101)This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously ... 
Multiobjectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions
(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. ... 
Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions
(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, ... 
Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation
(2018)The physicochemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. Online supervision of the turbidity point by means of ... 
On the relevance of preprocessing in predictive maintenance for 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, ... 
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutationbased combinatorial optimization problems
(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 note on the behavior of majority voting in multiclass domains with biased annotators
(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 ... 
Effects of reducing VMs management times on elastic applications
(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 ... 
Are the artificially generated instances uniform in terms of difficulty?
(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 ... 
Soft range information for network localization
(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 ... 
Detection of Sand Dunes on Mars Using a Regular Vinebased Classification Approach
(201808)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 ... 
Bayesian inference for algorithm ranking analysis
(20180830)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 ... 
Distancebased exponential probability models on constrained combinatorial optimization problems
(20180830)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 ...