Bayesian Network Classifiers for Damage Detection in Engineering Material

The automation of damage detection in engineering material using intelligent techniques (e.g. Neural networks) has not been matured enough to be practi- cable and needs more techniques to be implemented, improved, and developed. Nevertheless, the Neural networks have been implemented extensively for the damage detection, but in elementary ways. The damage detection and pre- diction are very important processes, since the damages have the potential of growing and leading to catastrophic loss of human life, and decrease in econ- omy (e.g. airline crashes, space shuttle explosions, and building collapses). Bayesian networks have been successfully implemented as classi¯ers in many research and industrial areas and they are used as models for representing un- certainty in knowledge domains. Nevertheless, they have not been thoroughly investigated and implemented such as Neural networks for the damage detec- tion. This thesis is dedicated to introduce them with the axiom of damage de- tection and implement them as a competitive probabilistic graphical model and as classi¯cation tools (Naijve bayes classi¯er and Bayesian network classi¯er) for the damage detection. The Bayesian networks have two-sided strengths: It is easy for humans to construct and to understand, and when communicated to a computer, they can easily be compiled. Changes in a system model should only induce local changes in a Bayesian network, where as system changes might require the design and training of an entirely new Neural network. The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). The state-of-the-art shows that most of the feature reduction techniques, if not all, which have been implemented for the damage detection are feature selection not extraction. Feature selection is less °exible than feature extrac- tion in that feature selection is, in fact, a special case of feature extraction (with a coe±cient of one for each selected feature and a coe±cient of zero for any of the other features). This explains why an optimal feature set ob- tained by feature selection may or may not yield a good classi¯cation results. To validate the classi¯ers and the proposed algorithm, two data sets were used, the ¯rst set represents voltage amplitudes of Lamb-waves produced and col- lected by sensors and actuators mounted on the surface of laminates contain di®erent arti¯cial damages. The second set is a vibration data from a type of ball bearing operating under di®erent ¯ve fault conditions. The Bayesian net- work classi¯ers and the proposed algorithm have been tested using the second set. The studies conducted in this research have shown that Bayesian networks as one of the most successful machine learning classi¯ers for the damage detection in general and the Naijve bayes classi¯er in particular. They have also shown their e±ciency when compared to Neural networks in domains of uncertainty. The studies have also shown the e®ectiveness and e±ciency of the proposed algorithm in reducing the number of the input features while increasing the accuracy of the classi¯er. These techniques will play vital role in damage de- tection in engineering material, specially in the smart materials, which require continuous monitoring of the system for damages

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Improving Neural Additive Models with Bayesian Principles

Neural additive models (NAMs) can improve the interpretability of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties and enable selection of relevant features and interactions. Approaching NAMs from a Bayesian perspective, we enhance them in three primary ways, namely by a) providing credible intervals for the individual additive sub-networks; b) estimating the marginal likelihood to perform an implicit selection of features via an empirical Bayes procedure; and c) enabling a ranking of feature pairs as candidates for second-order interaction in fine-tuned models. In particular, we develop Laplace-approximated NAMs (LA-NAMs), which show improved empirical performance on tabular datasets and challenging real-world medical tasks

Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neurons

Inferring the mechanisms underlying physiological and pathological processes in the brain from recorded electrical activity is challenging. Bayesian model selection and dynamic causal modelling aim to identify likely biophysical models to explain data and to fit the model parameters. Here, we use data generated by simulations to investigate the effectiveness of Bayesian model selection and dynamic causal modelling when applied at steady state in the frequency domain to identify and fit Jansen-Rit models. We first investigate the impact of the necessary assumption of linearity on the dynamics of the Jansen-Rit model. We then apply dynamic causal modelling and Bayesian model selection to data generated from simulations of linear neural mass models, non-linear neural mass models, and networks of discrete spiking neurons. Action potentials are a characteristic feature of neuronal dynamics but have not previously been explicitly included in simulations used to test Bayesian model selection or dynamic causal modelling. We find that the assumption of linearity abolishes the qualitative transitions seen as a function of the connectivity parameter in the original Jansen-Rit model. As with previous work, we find that the recovery procedures are effective when applied to data from linear Jansen-Rit neural mass models, however, when applying them to non-linear neural mass models and networks of discrete spiking neurons we find that their effectiveness is significantly reduced, suggesting caution is required when applying these methods.Comment: 18 pages, 15 figure

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studie