Oscar Cordon | University of Granada (original) (raw)
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Papers by Oscar Cordon
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IEEE Computational Intelligence Magazine, Feb 1, 2019
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Lecture Notes in Computer Science, 2022
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Information Sciences
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Proceedings of the Annual Hawaii International Conference on System Sciences, 2022
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Handbook on Craniofacial Superimposition, 2019
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2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Calibration is a crucial task on building valid models before exploiting their results. This proc... more Calibration is a crucial task on building valid models before exploiting their results. This process consists of adjusting the model parameters in order to obtain the desired outputs. Automatic calibration can be performed by using an optimization algorithm and a fitness function, which involves a deviation measure to compare the time series coming from the model. In this paper, we apply a memetic IPOP-CMA-ES for the calibration of an agent-based model and we study the effect of different deviation measures in this calibration problem. Classical metrics calculate the mean point-to-point error, but we also propose using an extension of dynamic time warping, which considers trend series evolution. In order to determine if calibrating with an specific metric leads to better solutions, we carry out an exhaustive experimentation by including statistical tests, analysis on the values of the calibrated parameters, and qualitative results. Our results show IPOP-CMA-ES obtains better performance than a genetic algorithm. In addition, MAE, MAPE and Soft-DTW are the metrics which report best results, although we get a similar behavior for all of them.
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Handbook on Craniofacial Superimposition, 2019
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Handbook on Craniofacial Superimposition, Nov 6, 2019
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Handbook on Craniofacial Superimposition, Nov 6, 2019
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Handbook on Craniofacial Superimposition, Nov 6, 2019
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Information Processing & Management, 2020
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Handbook on Craniofacial Superimposition, 2019
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Progress in Artificial Intelligence, 2019
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SSRN Electronic Journal, 2017
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International Journal of Approximate Reasoning, 2016
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Lecture Notes in Computer Science, 2016
Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mor... more Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of an individual and the analysis of their morphological correspondence. This research focused on the skull-face overlay stage with the aim of modeling the expert knowledge that is related to the existing anthropometric differences among landmarks and incorporating it into this stage. Consequently, we have moved from a single-objective optimization problem to a multiobjective optimization one aimed to reduce the distances between pairs of landmarks from each group independently. To tackle it, two classic approaches from the area of multicriteria decision making were used: weighted sum and lexicographical order. The results, which were obtained over a Ground Truth dataset, are promising in those cases where the forensic expert has located a large number of landmarks, and worse results than the state of the art method in cases with few landmarks.
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Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
IEEE Computational Intelligence Magazine, Feb 1, 2019
Bookmarks Related papers MentionsView impact
Lecture Notes in Computer Science, 2022
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Information Sciences
Bookmarks Related papers MentionsView impact
Proceedings of the Annual Hawaii International Conference on System Sciences, 2022
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Handbook on Craniofacial Superimposition, 2019
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Calibration is a crucial task on building valid models before exploiting their results. This proc... more Calibration is a crucial task on building valid models before exploiting their results. This process consists of adjusting the model parameters in order to obtain the desired outputs. Automatic calibration can be performed by using an optimization algorithm and a fitness function, which involves a deviation measure to compare the time series coming from the model. In this paper, we apply a memetic IPOP-CMA-ES for the calibration of an agent-based model and we study the effect of different deviation measures in this calibration problem. Classical metrics calculate the mean point-to-point error, but we also propose using an extension of dynamic time warping, which considers trend series evolution. In order to determine if calibrating with an specific metric leads to better solutions, we carry out an exhaustive experimentation by including statistical tests, analysis on the values of the calibrated parameters, and qualitative results. Our results show IPOP-CMA-ES obtains better performance than a genetic algorithm. In addition, MAE, MAPE and Soft-DTW are the metrics which report best results, although we get a similar behavior for all of them.
Bookmarks Related papers MentionsView impact
Handbook on Craniofacial Superimposition, 2019
Bookmarks Related papers MentionsView impact
Handbook on Craniofacial Superimposition, Nov 6, 2019
Bookmarks Related papers MentionsView impact
Handbook on Craniofacial Superimposition, Nov 6, 2019
Bookmarks Related papers MentionsView impact
Handbook on Craniofacial Superimposition, Nov 6, 2019
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Information Processing & Management, 2020
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Handbook on Craniofacial Superimposition, 2019
Bookmarks Related papers MentionsView impact
Progress in Artificial Intelligence, 2019
Bookmarks Related papers MentionsView impact
SSRN Electronic Journal, 2017
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International Journal of Approximate Reasoning, 2016
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Lecture Notes in Computer Science, 2016
Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mor... more Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of an individual and the analysis of their morphological correspondence. This research focused on the skull-face overlay stage with the aim of modeling the expert knowledge that is related to the existing anthropometric differences among landmarks and incorporating it into this stage. Consequently, we have moved from a single-objective optimization problem to a multiobjective optimization one aimed to reduce the distances between pairs of landmarks from each group independently. To tackle it, two classic approaches from the area of multicriteria decision making were used: weighted sum and lexicographical order. The results, which were obtained over a Ground Truth dataset, are promising in those cases where the forensic expert has located a large number of landmarks, and worse results than the state of the art method in cases with few landmarks.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact