Corpus ID: 15649155

A GENETIC ALGORITHM FOR LEARNING BAYESIAN NETWORK ADJACENCY MATRICES FROM DATA

@inproceedings{Perry2003AGA,
  title={A GENETIC ALGORITHM FOR LEARNING BAYESIAN NETWORK ADJACENCY MATRICES FROM DATA},
  author={B. Perry},
  year={2003}
}
In this thesis, we provide a general background for inference and learning, using Bayesian networks and genetic algorithms. We introduce Bayesian Networks in Java, a Java-based Bayesian network API that we have developed. We describe our research with structure learning using a genetic algorithm to search the space of adjacency matrices for a Bayesian network. We first instantiate the population using one of several methods: pure random sampling, perturbation or refinement of a candidate… Expand
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References

SHOWING 1-10 OF 18 REFERENCES
Learning Bayesian Networks: Search Methods and Experimental Results
TLDR
A metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994a,b,c) has a property useful for inferring causation from data and is described. Expand
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TLDR
A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown. Expand
Structure Learning of Bayesian Networks from Databases by Genetic Algorithms-Application to Time Series Prediction in Finance
TLDR
A genetic algorithm based method for constructing bayesian networks from databases that permits the generation of a complete structure if there is no expert for the domain studied and allows taking advantage of the knowledge about the domain by specifying connections in the network. Expand
Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
TLDR
This paper demonstrates how genetic algorithms can be used to discover the structure of a Bayesian network from a given database with cases by applying four different types of genetic algorithms to simulations of the ALARM Network. Expand
A Bayesian method for the induction of probabilistic networks from data
TLDR
This paper presents a Bayesian method for constructing probabilistic networks from databases, focusing on constructing Bayesian belief networks, and extends the basic method to handle missing data and hidden variables. Expand
Probabilistic reasoning in expert systems - theory and algorithms
TLDR
This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field the authors now call Bayesian networks and provides an insightful comparison of the two most prominent approaches to probability. Expand
Bayesian analysis in expert systems
TLDR
Using a real, moderately complex, medical example, it is illustrated how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context. Expand
Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm
TLDR
The masking GA/knn feature selection method can efficiently examine noisy, complex, and high-dimensionality datasets to find combinations of features which classify the data more accurately and result in equivalent or better classification accuracy using fewer features. Expand
Wrappers for Feature Subset Selection
TLDR
The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection. Expand
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