2012
Oquendo, Maria A; Baca-García, Enrique; Artés-Rodríguez, Antonio; Perez-Cruz, Fernando; Galfalvy, H C; Blasco-Fontecilla, Hilario; Madigan, D; Duan, N
Machine Learning and Data Mining: Strategies for Hypothesis Generation Artículo de revista
En: Molecular psychiatry, vol. 17, no 10, pp. 956–959, 2012, ISSN: 1476-5578.
Resumen | Enlaces | BibTeX | Etiquetas: Artificial Intelligence, Biological, Data Mining, Humans, Mental Disorders, Mental Disorders: diagnosis, Mental Disorders: therapy, Models
@article{Oquendo2012,
title = {Machine Learning and Data Mining: Strategies for Hypothesis Generation},
author = {Maria A Oquendo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando Perez-Cruz and H C Galfalvy and Hilario Blasco-Fontecilla and D Madigan and N Duan},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22230882},
issn = {1476-5578},
year = {2012},
date = {2012-01-01},
journal = {Molecular psychiatry},
volume = {17},
number = {10},
pages = {956--959},
abstract = {Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an \'{o}rganic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.},
keywords = {Artificial Intelligence, Biological, Data Mining, Humans, Mental Disorders, Mental Disorders: diagnosis, Mental Disorders: therapy, Models},
pubstate = {published},
tppubtype = {article}
}
Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an órganic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.
2011
Santiago-Mozos, Ricardo; Perez-Cruz, Fernando; Artés-Rodríguez, Antonio
Extended Input Space Support Vector Machine Artículo de revista
En: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 22, no 1, pp. 158–163, 2011, ISSN: 1941-0093.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithms, Artificial Intelligence, Automated, Automated: standards, Computer Simulation, Computer Simulation: standards, Neural Networks (Computer), Pattern recognition, Problem Solving, Software Design, Software Validation
@article{Santiago-Mozos2011,
title = {Extended Input Space Support Vector Machine},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P38_2011_Extended Input Space Support Vector Machine.pdf
http://www.ncbi.nlm.nih.gov/pubmed/21095866},
issn = {1941-0093},
year = {2011},
date = {2011-01-01},
journal = {IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council},
volume = {22},
number = {1},
pages = {158--163},
abstract = {In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from the same class to reduce the probability of error of the final decision. From the point of view of hypothesis testing, the solution is given by the Neyman-Pearson lemma. However, there is no equivalent result to the Neyman-Pearson lemma when the likelihoods are unknown, and we are given a training dataset. In this brief, we explore two alternatives. First, we combine the soft (probabilistic) outputs of a given classifier to produce a consensus labeling for K test samples. In the second approach, we build a new classifier that directly computes the label for K test samples. For this second approach, we need to define an extended input space training set and incorporate the known symmetries in the classifier. This latter approach gives more accurate results, as it only requires an accurate classification boundary, while the former needs an accurate posterior probability estimate for the whole input space. We illustrate our results with well-known databases.},
keywords = {Algorithms, Artificial Intelligence, Automated, Automated: standards, Computer Simulation, Computer Simulation: standards, Neural Networks (Computer), Pattern recognition, Problem Solving, Software Design, Software Validation},
pubstate = {published},
tppubtype = {article}
}
In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from the same class to reduce the probability of error of the final decision. From the point of view of hypothesis testing, the solution is given by the Neyman-Pearson lemma. However, there is no equivalent result to the Neyman-Pearson lemma when the likelihoods are unknown, and we are given a training dataset. In this brief, we explore two alternatives. First, we combine the soft (probabilistic) outputs of a given classifier to produce a consensus labeling for K test samples. In the second approach, we build a new classifier that directly computes the label for K test samples. For this second approach, we need to define an extended input space training set and incorporate the known symmetries in the classifier. This latter approach gives more accurate results, as it only requires an accurate classification boundary, while the former needs an accurate posterior probability estimate for the whole input space. We illustrate our results with well-known databases.
2007
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio
Maximization of Mutual Information for Supervised Linear Feature Extraction Artículo de revista
En: IEEE Transactions on Neural Networks, vol. 18, no 5, pp. 1433–1441, 2007, ISSN: 1045-9227.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors
@article{Leiva-Murillo2007,
title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},
issn = {1045-9227},
year = {2007},
date = {2007-01-01},
journal = {IEEE Transactions on Neural Networks},
volume = {18},
number = {5},
pages = {1433--1441},
publisher = {IEEE},
abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},
keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.