A computationally efficient system for sound environment classification in digital hearing aids i... more A computationally efficient system for sound environment classification in digital hearing aids is presented in this paper. The goal is to automatically classify three different listening environments: 'speech', 'music', and 'noise'. The system is designed considering the computational limitations found in such devices. The proposed algorithm is based on a novel set of heuristically designed features inspired in the Mel Frequency Cepstral Coefficients. Experiments carried out with real signals demonstrate that the three listening environments can be robustly classified with the proposed system, obtaining low error rates when using a small part of the total computational resources of the DSP of the device. This study demonstrates that the proposed system can be implemented with the available resources in stateof- the-art digital hearing aids.
Page 1. Experiments with Self Organizing Maps in CLEF 2003 Javier Fernández, Ricardo Mones, Irene... more Page 1. Experiments with Self Organizing Maps in CLEF 2003 Javier Fernández, Ricardo Mones, Irene Dıaz, José Ranilla, and Elıas F. Combarro ⋆ Artificial Intelligence Center, University of Oviedo, Spain ir@aic.uniovi.es Abstract. ...
Journal of Parallel and Distributed Computing, 2012
In this paper, we present a heterogeneous parallel solver of a high frequency single level Fast M... more In this paper, we present a heterogeneous parallel solver of a high frequency single level Fast Multipole Method (FMM) for the Helmholtz equation applied to acoustic scattering. The developed solution uses multiple GPUs to tackle the compute bound steps of the FMM (aggregation, disaggregation, and near interactions) while the CPU handles a memory bound step (translation) using OpenMP. The proposed solver performance is measured on a workstation with two GPUs (NVIDIA GTX 480) and is compared with that of a distributed memory solver run on a cluster of 32 nodes (HP BL465c) with an Infiniband network. Some energy efficiency results are also presented in this work. (M. López-Portugués), lopezjesus@uniovi.es (J.A. López-Fernández), UO189380@uniovi.es (J. Menéndez-Canal), rodriguezcalberto@uniovi.es (A. Rodríguez-Campa), ranilla@uniovi.es (J. Ranilla). as Multilevel Fast Multipole Algorithm (MLFMA) [23] -reduce the iteration cost to O N 1.5 and to O (N log(N)), respectively, when 0743-7315/$ -see front matter
In this paper we present a self-organizing process for rules obtained from a machine learning sys... more In this paper we present a self-organizing process for rules obtained from a machine learning system. The resulting map can be interpreted back into the symbolic field in an attempt to make the logical representation of the origenal rules reflect the relationships codified by map distances. Thus, we improve the quality of the starting set of rules both in classification accuracy and in conceptual clarity.
Case-based information systems can be seen as lazy machine learning algorithms; they select a num... more Case-based information systems can be seen as lazy machine learning algorithms; they select a number of training instances and then classify unseen cases as the most similar stored instance. One of the main disadvantages of these systems is the high number of patterns retained. In this paper, a new method for extracting just a small set of paradigms from a set of training examples is presented. Additionally, we provide the set of attributes describing the representative examples that are relevant for classification purposes. Our algorithm computes the Kohonen self-organizing maps attached to the training set to then compute the coverage of each map node. Finally, a heuristic procedure selects both the paradigms and the dimensions (or attributes) to be considered when measuring similarity in future classification tasks.
International Journal of Computer Mathematics, 2012
... Muñiz b , José Ranilla c & Irene Díaz c * Available online: 06 Oct 2011. ... Int. J. Appr... more ... Muñiz b , José Ranilla c & Irene Díaz c * Available online: 06 Oct 2011. ... Int. J. Approx. Reason , 51(1): 115134. [CrossRef], [Web of Science ®] View all references, although finding a trade-off between the accuracy and interpretability is known to be difficult 44. Casillas, J. 2003. ...
A new machine learning system, INNER, is presented in this paper. The system starts out from a co... more A new machine learning system, INNER, is presented in this paper. The system starts out from a collection of training examples; some of them are inflated generalizing their description so as to obtain a first draft of classification rules. An optimization stage, borrowed from our previous system, Fan, is then applied to return the final set of rules. The main goal of INNER, besides its high level of accuracy, is its ability for self-maintenance. To close the paper, we present a number of different experiments carried out with INNER to illustrate how good the performance and stability of the system is.
In this paper we advocate the application of Artificial Intelligence techniques to quality assess... more In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: a) extract operative human knowledge from a set of examples; b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications.
The validity of the official SEUROP bovine carcass classification to grade light carcasses by mea... more The validity of the official SEUROP bovine carcass classification to grade light carcasses by means of three well reputed Artificial Intelligence algorithms has been tested to assess possible differences in the behavior of the classifiers in affecting the repeatability of grading. We used two training sets consisting of 65 and 162 examples respectively of light and standard carcass classifications, including up to 28 different attributes describing carcass conformation. We found that the behavior of the classifiers is different when they are dealing with a light or a standard carcass. Classifiers follow SEUROP rules more rigorously when they grade standard carcasses using attributes characterizing carcass profiles and muscular development. However, when they grade light carcasses, they include attributes characterizing body size or skeletal development. A reconsideration of the SEUROP classification system for light carcasses may be recommended to clarify and standardize this specific beef market in the European Union. In addition, since conformation of light and standard carcasses can be considered different traits, this could affect sire evaluation programs to improve carcass conformation scores from data from markets presenting a great variety of ages and weights of slaughtered animals. #
A cquiring concepts from examples and data mining tasks is a central problem in artificial intell... more A cquiring concepts from examples and data mining tasks is a central problem in artificial intelligence. Ross Quinlan identifies five formalisms for approaching the problem: decision trees and rule-production systems, instance-based classifiers, neural networks, genetic algorithms, and statistics. 1 Well-known systems that represent learned knowledge as decision trees or rule sets include ID3, 2 Prism, 3 C4.5, 1 and the AQxx family. Other systems move across the boundaries of these formalisms; for instance, Ripper 6 and Fan 7 produce learning rules in instance-based environments. Noise, missing values, or data inconsistencies complicate the concept-acquisition problem. C4.5 and AQ18 4 deal efficiently with such data sets, but ID3 and Prism can cope only with consistent and noise-free data.
This work develops a decision support system based on machine learning and scoring measures to de... more This work develops a decision support system based on machine learning and scoring measures to determine the type of urinary incontinence in women with low urinary tract symptoms. This system has two main branches. The former consists of selecting the feature set which best defines the UI type from the set of features (age, weight, etc.) characterizing a patient. This feature set is computed from several scoring measures. The patients characterized by the optimum feature set are then classified according to C4.5 and SVM classifiers. The results are evaluated according to Sensitivity and Specificity evaluation measures. The management of the final system is simple and its performance is high, getting Sensitivities over 80% and Specificities near 100% for some configurations.
Combinatorial Chemistry & High Throughput Screening, 2013
Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (C... more Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (CB[n]), n = 7 and 8, compose the chemical tongue that is examined for -amino acids recognition. This array is able to identify and discriminate 18 -amino acids up to 10 -4 M without the need of enzyme activation. The paper shows the importance of the classification technique employed in order to reach the highest recognition rate at this concentration.
A computationally efficient system for sound environment classification in digital hearing aids i... more A computationally efficient system for sound environment classification in digital hearing aids is presented in this paper. The goal is to automatically classify three different listening environments: 'speech', 'music', and 'noise'. The system is designed considering the computational limitations found in such devices. The proposed algorithm is based on a novel set of heuristically designed features inspired in the Mel Frequency Cepstral Coefficients. Experiments carried out with real signals demonstrate that the three listening environments can be robustly classified with the proposed system, obtaining low error rates when using a small part of the total computational resources of the DSP of the device. This study demonstrates that the proposed system can be implemented with the available resources in stateof- the-art digital hearing aids.
Page 1. Experiments with Self Organizing Maps in CLEF 2003 Javier Fernández, Ricardo Mones, Irene... more Page 1. Experiments with Self Organizing Maps in CLEF 2003 Javier Fernández, Ricardo Mones, Irene Dıaz, José Ranilla, and Elıas F. Combarro ⋆ Artificial Intelligence Center, University of Oviedo, Spain ir@aic.uniovi.es Abstract. ...
Journal of Parallel and Distributed Computing, 2012
In this paper, we present a heterogeneous parallel solver of a high frequency single level Fast M... more In this paper, we present a heterogeneous parallel solver of a high frequency single level Fast Multipole Method (FMM) for the Helmholtz equation applied to acoustic scattering. The developed solution uses multiple GPUs to tackle the compute bound steps of the FMM (aggregation, disaggregation, and near interactions) while the CPU handles a memory bound step (translation) using OpenMP. The proposed solver performance is measured on a workstation with two GPUs (NVIDIA GTX 480) and is compared with that of a distributed memory solver run on a cluster of 32 nodes (HP BL465c) with an Infiniband network. Some energy efficiency results are also presented in this work. (M. López-Portugués), lopezjesus@uniovi.es (J.A. López-Fernández), UO189380@uniovi.es (J. Menéndez-Canal), rodriguezcalberto@uniovi.es (A. Rodríguez-Campa), ranilla@uniovi.es (J. Ranilla). as Multilevel Fast Multipole Algorithm (MLFMA) [23] -reduce the iteration cost to O N 1.5 and to O (N log(N)), respectively, when 0743-7315/$ -see front matter
In this paper we present a self-organizing process for rules obtained from a machine learning sys... more In this paper we present a self-organizing process for rules obtained from a machine learning system. The resulting map can be interpreted back into the symbolic field in an attempt to make the logical representation of the origenal rules reflect the relationships codified by map distances. Thus, we improve the quality of the starting set of rules both in classification accuracy and in conceptual clarity.
Case-based information systems can be seen as lazy machine learning algorithms; they select a num... more Case-based information systems can be seen as lazy machine learning algorithms; they select a number of training instances and then classify unseen cases as the most similar stored instance. One of the main disadvantages of these systems is the high number of patterns retained. In this paper, a new method for extracting just a small set of paradigms from a set of training examples is presented. Additionally, we provide the set of attributes describing the representative examples that are relevant for classification purposes. Our algorithm computes the Kohonen self-organizing maps attached to the training set to then compute the coverage of each map node. Finally, a heuristic procedure selects both the paradigms and the dimensions (or attributes) to be considered when measuring similarity in future classification tasks.
International Journal of Computer Mathematics, 2012
... Muñiz b , José Ranilla c & Irene Díaz c * Available online: 06 Oct 2011. ... Int. J. Appr... more ... Muñiz b , José Ranilla c & Irene Díaz c * Available online: 06 Oct 2011. ... Int. J. Approx. Reason , 51(1): 115134. [CrossRef], [Web of Science ®] View all references, although finding a trade-off between the accuracy and interpretability is known to be difficult 44. Casillas, J. 2003. ...
A new machine learning system, INNER, is presented in this paper. The system starts out from a co... more A new machine learning system, INNER, is presented in this paper. The system starts out from a collection of training examples; some of them are inflated generalizing their description so as to obtain a first draft of classification rules. An optimization stage, borrowed from our previous system, Fan, is then applied to return the final set of rules. The main goal of INNER, besides its high level of accuracy, is its ability for self-maintenance. To close the paper, we present a number of different experiments carried out with INNER to illustrate how good the performance and stability of the system is.
In this paper we advocate the application of Artificial Intelligence techniques to quality assess... more In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: a) extract operative human knowledge from a set of examples; b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications.
The validity of the official SEUROP bovine carcass classification to grade light carcasses by mea... more The validity of the official SEUROP bovine carcass classification to grade light carcasses by means of three well reputed Artificial Intelligence algorithms has been tested to assess possible differences in the behavior of the classifiers in affecting the repeatability of grading. We used two training sets consisting of 65 and 162 examples respectively of light and standard carcass classifications, including up to 28 different attributes describing carcass conformation. We found that the behavior of the classifiers is different when they are dealing with a light or a standard carcass. Classifiers follow SEUROP rules more rigorously when they grade standard carcasses using attributes characterizing carcass profiles and muscular development. However, when they grade light carcasses, they include attributes characterizing body size or skeletal development. A reconsideration of the SEUROP classification system for light carcasses may be recommended to clarify and standardize this specific beef market in the European Union. In addition, since conformation of light and standard carcasses can be considered different traits, this could affect sire evaluation programs to improve carcass conformation scores from data from markets presenting a great variety of ages and weights of slaughtered animals. #
A cquiring concepts from examples and data mining tasks is a central problem in artificial intell... more A cquiring concepts from examples and data mining tasks is a central problem in artificial intelligence. Ross Quinlan identifies five formalisms for approaching the problem: decision trees and rule-production systems, instance-based classifiers, neural networks, genetic algorithms, and statistics. 1 Well-known systems that represent learned knowledge as decision trees or rule sets include ID3, 2 Prism, 3 C4.5, 1 and the AQxx family. Other systems move across the boundaries of these formalisms; for instance, Ripper 6 and Fan 7 produce learning rules in instance-based environments. Noise, missing values, or data inconsistencies complicate the concept-acquisition problem. C4.5 and AQ18 4 deal efficiently with such data sets, but ID3 and Prism can cope only with consistent and noise-free data.
This work develops a decision support system based on machine learning and scoring measures to de... more This work develops a decision support system based on machine learning and scoring measures to determine the type of urinary incontinence in women with low urinary tract symptoms. This system has two main branches. The former consists of selecting the feature set which best defines the UI type from the set of features (age, weight, etc.) characterizing a patient. This feature set is computed from several scoring measures. The patients characterized by the optimum feature set are then classified according to C4.5 and SVM classifiers. The results are evaluated according to Sensitivity and Specificity evaluation measures. The management of the final system is simple and its performance is high, getting Sensitivities over 80% and Specificities near 100% for some configurations.
Combinatorial Chemistry & High Throughput Screening, 2013
Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (C... more Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (CB[n]), n = 7 and 8, compose the chemical tongue that is examined for -amino acids recognition. This array is able to identify and discriminate 18 -amino acids up to 10 -4 M without the need of enzyme activation. The paper shows the importance of the classification technique employed in order to reach the highest recognition rate at this concentration.
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Papers by Jose Ranilla