Daniele Codecasa

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The goal of this paper is to present four new parallel and distributed particle swarm optimization methods. and to experimentally compare their performances. These methods include a genetic algorithm whose individuals are co-evolving swarms, a different multi-swarm system and their respective variants enriched by adding a repulsive component to the(More)
Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter(More)
Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not change over time. This paper introduces the CTBNCToolkit: an open source Java toolkit which provides a stand-alone application for temporal classification and a library for(More)
The current technological trend depicts a scenario in which space, and more generally the environment in which the computation takes place, represents a key aspect that must be considered in order to improve systems context awareness. Reasoning about such context can be interpreted as spatial reasoning, which means not only to be able to carry out(More)
Classification and clustering of streaming data are relevant in finance, computer science, and engineering while they are becoming increasingly important in medicine and biology. Streaming data are analyzed with algorithms and models capable to represent dynamics, sequences and time. Dynamic Bayesian networks and hidden Markov models are commonly used to(More)
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