Optimizing Contextual-Based Optimum-Forest Classification through Swarm Intelligence
Several works have been conducted in order to improve classification problems. However, a considerable amount of them do not consider the contextual information in the learning process, which may help the classification step by providing additional information about the relation between a sample and its neighbourhood. Recently, a previous work have proposed a hybrid approach between Optimum-Path Forest classifier and Markov Random Fields (OPF-MRF) aiming to provide contextual information for this classifier. However, the contextual information was restricted to a spatial/temporal-dependent parameter, which has been empirically chosen in that work. We propose here an improvement of OPF-MRF by modelling the problem of finding such parameter as a swarm-based optimization task, which is carried out Particle Swarm Optimization and Harmony Search. The results have been conducted over the classification of Magnetic Ressonance Images of the brain, and the proposed approach seemed to find close results to the ones obtained by an exhaustive search for this parameter, but much faster for that.
KeywordsMagnetic Resonance Images Optimum-Path Forest Markov Random Fields Particle Swarm Optimization Harmony Search
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