Publications

@article{BUSTILLO2018b,
title = {Smart optimization of a friction-drilling process based on boosting ensembles},
author = {Andres Bustillo and Gorka Urbikain and Jose M Perez and Octavio M Pereira and Luis Lopez N de Lacalle},
url = {http://www.sciencedirect.com/science/article/pii/S0278612518301249},
doi = {https://doi.org/10.1016/j.jmsy.2018.06.004},
issn = {0278-6125},
year = {2018},
date = {2018-08-16},
journal = {Journal of Manufacturing Systems},
abstract = {Form and friction drilling techniques are now promising alternatives in light and medium boilermaking that will very probably supersede conventional drilling techniques, as rapid and economic solutions for producing nutless bolted joints. Nonetheless, given the number of cutting parameters involved, optimization of the process requires calibration of the main input parameters in relation to the desired output values. Among these values, the gap between plates determines the service life of the joint. In this paper, a suitable smart manufacturing strategy for real industrial conditions is proposed, where it is necessary to identify the most accurate machine-learning technique to process experimental datasets of a small size. The strategy is first to generate a small-size dataset under real industrial conditions, then the gap is discretized taking into account the specific industrial needs of this quality indicator for each product. Finally, the different machine learning models are tested and fine-tuned to ascertain the most accurate model at the lowest cost. The strategy is validated with a 48 condition-dataset where only feed-rate and rotation speed are used as inputs and the gap as the output. The results on this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than artificial neural networks.},
keywords = {Boosting, Ensembles, Friction drilling, Gap prediction, Small-size dataset},
pubstate = {published},
tppubtype = {article}
}

@article{DiezPastor2014,
title = {Tree ensemble construction using a GRASP-based heuristic and annealed randomness},
author = {José Francisco Díez-Pastor and César García-Osorio and Juan José Rodríguez},
url = {http://www.sciencedirect.com/science/article/pii/S1566253514000141},
doi = {10.1016/j.inffus.2014.01.009},
issn = {1566-2535},
year = {2014},
date = {2014-01-01},
journal = {Information Fusion},
volume = {20},
number = {0},
pages = {189--202},
abstract = {Abstract Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness. The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named ``GAR'', GRASP with annealed randomness. The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method. The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.},
keywords = {Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, GRASP metahuristic, Random forest},
pubstate = {published},
tppubtype = {article}
}

@article{PedrajasOsorio2013,
title = {Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections},
author = {Nicolás García-Pedrajas and César García-Osorio},
url = {http://dx.doi.org/10.1007/s13748-012-0028-4},
doi = {10.1007/s13748-012-0028-4},
issn = {2192-6352},
year = {2013},
date = {2013-01-01},
journal = {Progress in Artificial Intelligence},
volume = {2},
number = {1},
pages = {29-44},
publisher = {Springer-Verlag},
keywords = {Boosting, Class-imbalanced problems, Data Mining, Real-coded genetic algorithms},
pubstate = {published},
tppubtype = {article}
}

@article{RFW2012,
title = {Random Feature Weights for Decision Tree Ensemble Construction},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio and Nicolás García-Pedrajas},
doi = {10.1016/j.inffus.2010.11.004},
issn = {1566-2535},
year = {2012},
date = {2012-01-01},
journal = {Information Fusion},
volume = {13},
number = {1},
pages = {20-30},
keywords = {Bagging, Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, Random forest},
pubstate = {published},
tppubtype = {article}
}

@article{ensemblesProjections2011,
title = {Constructing ensembles of classifiers using supervised projection methods based on misclassified instances},
author = {Nicolás García-Pedrajas and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/B6V03-50GJ2J0-7/2/6b1890282b8bfb900f1174dc7a027a9c},
doi = {10.1016/j.eswa.2010.06.072},
issn = {0957-4174},
year = {2011},
date = {2011-01-01},
journal = {Expert Systems with Applications},
volume = {38},
number = {1},
pages = {343--359},
keywords = {Boosting, Classification, Data Mining, Linear projections, Subspace methods},
pubstate = {published},
tppubtype = {article}
}

@article{cgosorio07boosting,
title = {Nonlinear ``boosting'' projections for ensemble construction},
author = {Nicolás García-Pedrajas and César García-Osorio and Colin Fyfe},
url = {http://jmlr.csail.mit.edu/papers/volume8/garcia-pedrajas07a/garcia-pedrajas07a.pdf},
issn = {1532-4435},
year = {2007},
date = {2007-01-01},
journal = {Journal of Machine Learning Research},
volume = {8},
pages = {1--33},
abstract = {In this paper we propose a novel approach for ensemble construction based on the use of nonlinear
projections to achieve both accuracy and diversity of individual classifiers. The proposed approach
combines the philosophy of boosting, putting more effort on difficult instances, with the basis of
the random subspace method. Our main contribution is that instead of using a random subspace,
we construct a projection taking into account the instances which have posed most difficulties to
previous classifiers. In this way, consecutive nonlinear projections are created by a neural network
trained using only incorrectly classified instances. The feature subspace induced by the hidden layer
of this network is used as the input space to a new classifier. The method is compared with bagging
and boosting techniques, showing an improved performance on a large set of 44 problems from the
UCI Machine Learning Repository. An additional study showed that the proposed approach is less
sensitive to noise in the data than boosting methods.},
keywords = {Boosting, Classifier ensembles, Data Mining, Ensemble methods, Neural networks, Nonlinear projections},
pubstate = {published},
tppubtype = {article}
}