Instance and feature weighted k-nearest-neighbors algorithm

We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier. We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray gene expression problems. Our results show increases in stability for most subset sizes and most problems, without compromising prediction accuracy.Peer ReviewedPostprint (published version

Exploiting the accumulated evidence for gene selection in microarray gene expression data

Machine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an important role in the modeling process, since these tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this particular scenario, it is extremely important to select genes by taking into account the possible interactions with other gene subsets. This paper shows that, by accumulating the evidence in favour (or against) each gene along the search process, the obtained gene subsets may constitute better solutions, either in terms of predictive accuracy or gene size, or in both. The proposed technique is extremely simple and applicable at a negligible overhead in cost.Postprint (published version

Size distribution and waiting times for the avalanches of the Cell Network Model of Fracture

The Cell Network Model is a fracture model recently introduced that resembles the microscopical structure and drying process of the parenchymatous tissue of the Bamboo Guadua angustifolia. The model exhibits a power-law distribution of avalanche sizes, with exponent -3.0 when the breaking thresholds are randomly distributed with uniform probability density. Hereby we show that the same exponent also holds when the breaking thresholds obey a broad set of Weibull distributions, and that the humidity decrements between successive avalanches (the equivalent to waiting times for this model) follow in all cases an exponential distribution. Moreover, the fraction of remaining junctures shows an exponential decay in time. In addition, introducing partial breakings and cumulative damages induces a crossover behavior between two power-laws in the avalanche size histograms. This results support the idea that the Cell Network Model may be in the same universality class as the Random Fuse Model

Implementación de un sistema de captura de gestos usando un leap motion y redes neuronales para su clasificación

Se describe a continuación el proceso llevado a cabo para la implementación de un sistema de captura de gestos usando un sensor propietario y redes neuronales, el prototipo se construyó usando tecnologías web y algunos frameworks para javascript junto con un sensor llamado “leap motion” usado como dispositivo de entrada. Los datos otorgados por el sensor son la base para el entrenamiento de las redes neuronales a usar. En las pruebas se compararon los 2 modelos; clasificación multi-etiqueta(ML) y clasificación multi-clase(MC), teniendo en cuenta factores como: tiempos de entrenamiento, error aproximado y cantidad de datos