Learning from non-stationary data using a growing network of prototypes

Proceeding of: 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, 20-23 June 2013Learning from non-stationary data requires methods that are able to deal with a continuous stream of data instances, possibly of infinite size, where the class distributions are potentially drifting over time. For handling such datasets, we are proposing a new method that incrementally creates and adapts a network of prototypes for classifying complex data received in an online fashion. The algorithm includes both an accuracy-based and time-based forgetting mechanisms that ensure that the model size does not grow indefinitely with large datasets. We have performed tests on seven benchmarking datasets for comparing our proposal with several approaches found in the literature, including ensemble algorithms associated to two different base classifiers. Performances obtained show that our algorithm is comparable to the best of the ensemble classifiers in terms of accuracy/time trade-off. Moreover, our approach appears to have significant advantages for dealing with data that has a complex, non-linearly separable topology.Spanish Ministry of Science and Innovation under the project MOVES, grant reference TIN2011-28336, and NSERC-CanadaThis article has been funded by the Spanish Ministry of Science and Innovation under the project MOVES with grant reference TIN2011-28336, and NSERC-Canada.Publicad

Automatic Detection of Expanding HI Shells Using Artificial Neural Networks

The identification of expanding HI shells is difficult because of their variable morphological characteristics. The detection of HI bubbles on a global scale therefore never has been attempted. In this paper, an automatic detector for expanding HI shells is presented. The detection is based on the more stable dynamical characteristics of expanding shells and is performed in two stages. The first one is the recognition of the dynamical signature of an expanding bubble in the velocity spectra, based on the classification of an artificial neural network. The pixels associated with these recognized spectra are identified on each velocity channel. The second stage consists in looking for concentrations of those pixels that were firstly pointed out, and to decide if they are potential detections by morphological and 21-cm emission variation considerations. Two test bubbles are correctly detected and a potentially new case of shell that is visually very convincing is discovered. About 0.6% of the surveyed pixels are identified as part of a bubble. These may be false detections, but still constitute regions of space with high probability of finding an expanding shell. The subsequent search field is thus significantly reduced. We intend to conduct in the near future a large scale HI shells detection over the Perseus Arm using our detector.Comment: 39 pages, 11 figures, accepted by PAS

La corrélation régionale, la comparaison par programmation dynamique et la comparaison d'arbres : trois procédures pour mesurer la similitude de signaux

Algorithme de correlation régionale -- Algorithme de comparaison par programmation dynamique -- Algorithme de comparaison

The Master-Slave Architecture for Evolutionary Computations Revisited

processing nodes. In contrast, the master-slave model has all required features. One issue that needs to be addressed, however, is its ability to scale with a large number of slave nodes, knowing that there is a communication bottleneck with the master node. In the rest of this short paper, we build a mathematical model of the masterslave and show that, given current Local Area Network (LAN) technologies, a quite large PDEC can be built before reaching this bottleneck. For real world applications, assuming that the time needed for fitness evaluation is the dominant time factor for evolutionary algorithms, the speedup of a master-slave system over that of a single processor can be modeled by NT f /T p , where N is the population size, T f is the time needed to evaluate the fitness of a single individual, and T p is the time needed to evaluate all individuals using P processors. Possible distribution policies range from separating the population into P sets and sending each of them t

International Journal on Artificial Intelligence Tools c ○ World Scientific Publishing Company GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS: PRINCIPLES AND CASE-STUDY

This paper deals with the need for generic software development tools in evolutionary computations (EC). These tools will be essential for the next generation of evolutionary algorithms where application designers and researchers will need to mix different combinations of traditional EC (e.g. genetic algorithms, genetic programming, evolutionary strategies, etc.), or to create new variations of these EC, in order to solve complex real world problems. Six basic principles are proposed to guide the development of such tools. These principles are then used to evaluate six freely available, widely used EC software tools. Finally, the design of Open BEAGLE, the framework developed by the authors, is presented in more detail