Browsing Machine Learning by Title
Now showing items 21-40 of 48
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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- ... -
Improved Quantification of Important Beer Quality Parameters based on Non-linear Calibration Methods applied to FT-MIR Spectra
(Analytical and Bioanalytical Chemistry, 2016-07-14)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 many-objective optimization: the I-Multi algorithm as a case study
(Swarm Intelligence, 2017-03-30)A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies ... -
Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016-01-01)In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness ... -
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 ... -
Learning to classify software defects from crowds: a novel approach
(Applied Soft Computing, 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 ... -
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 ... -
Measuring the Class-imbalance Extent of Multi-class Problems
(Pattern Recognition Letter, 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 ... -
Multi-objective Optimization of Orbit Transfer Trajectory Using Imperialist Competitive Algorithm
(IEEE Aerospace Conference, 2017-03-04)This paper proposes a systematic direct approach to carry out effective multi-objective optimization of space orbit transfer with high-level thrust acceleration. The objective is to provide a transfer trajectory with ... -
Multi-objectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions
(Evolutionary Computation, 2017-12)In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. ... -
Nature-inspired approaches for distance metric learning in multivariate time series classification
(IEEE Congress on Evolutionary Computation (CEC), 2017-07)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 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 ... -
A note on the Boltzmann distribution and the linear ordering problem
(Lecture Notes in Computer Science, 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 ... -
A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems
(2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016-11-14)In the so-called 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 non-intrusive load monitoring in smart grids
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015-12-31)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, 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. ... -
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, ... -
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 ... -
An optimization tool to design the field of a Solar Power Tower plant allowing heliostats of different sizes
(International Journal of Energy Research, 2016-10-30)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, 2015-12-31)Evolutionary algorithm-based unmanned aerial vehicle (UAV) path planners have been extensively studied for their effectiveness and flexibility. However, they still suffer from a drawback that the high-quality waypoints in ...