Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

We present a new approach to structure learning in the field of Bayesian networks: we tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer

Distributed Markov localisation for probabilistic behaviour activation

Probabilistic methods offer the necessary tools with a sound theoretical basis for handling self localisation but they are generally applied to rigid environment representations and thereby, they are hardly capable of coping with dynamic environments. Our current research effort aims to narrow the gap between behaviour based navigation and probabilistic methods. This paper presents a distributed self-localisation system in semi-structured environments

Main peaks from the fiber ODFs estimated in the “HARDI Reconstruction Challenge 2013” phantom.

<p>Visualization of the main peaks extracted from the fiber ODFs reconstructed from the SMF-based data generated with SNR = 20 in a complex region of the “HARDI Reconstruction Challenge 2013” phantom. Results are based on reconstructions using 400 iterations. Peaks are visualized as thin cylinders.</p

Reconstruction accuracy of RUMBA-SD and dRL-SD measured in phantoms with different volume fractions.

<p>Reconstruction accuracy of RUMBA-SD (blue color) and dRL-SD (red color) is shown in terms of the volume fraction of the smaller fiber bundle (upper panel) and the success rate (middle panel) in the 41 synthetic phantoms with inter-fiber angle equal to 70 degrees, using different volume fractions. The lower panel shows results similar to those depicted in the upper panel but considering only voxels where the two fiber bundles were detected. The discontinuous diagonal black line in the upper and lower panels represents the ideal result as a reference. The continuous coloured lines in each plot denote the mean values for each method. The semi-transparent coloured bands represent the values within one standard deviation to both sides of the mean. Results refer to the datasets with SNR = 15 and dictionary created with the true diffusivities.</p

Main peaks in the 45-degrees phantom data.

<p>Main peaks extracted from the fiber ODFs estimated in the phantom data with inter-fiber angle equal to 45 degrees and Rician noise with a SNR = 15 are shown. Results are based on reconstructions using 200 iterations. Peaks are visualized as thin cylinders.</p

Reconstruction accuracy levels of RUMBA-SD+TV and dRL-SD+TV.

<p>Reconstruction accuracy of RUMBA-SD+TV (blue color) and dRL-SD+TV (red color) is shown in terms of the angular error (θ) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138910#pone.0138910.e041" target="_blank">Eq (17)</a>) and the volume fraction error (Δ<i>f</i>) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138910#pone.0138910.e042" target="_blank">Eq (18)</a>) as a function of the inter-fiber angle in the 90 synthetic phantoms. Continuous lines are the mean values for each method, and semi-transparent coloured bands contain values within one standard deviation on both sides of the mean. This analysis is based on a dictionary created with the same diffusivities used to generate the data with a SNR = 15.</p