|Title||Discriminative Learning of Bayesian Network Classifiers|
|Related Course(s)||Advanced Data Mining, Learning from Data|
|Description||Bayesian Networks (BNs) are popular models for representing probabilistic knowledge,
and reasoning with uncertainty.
Automatic construction of BNs from a set of example data is an active area of research. In many applications of BNs,
we are mainly interested in predicting the value of one particular variable (the class) from the remaining variables (the attributes).
Such BNs are called Bayesian Network Classifiers (BNCs). To fit a single BNC to a data set, potentially time consuming iterative optimization algorithms are required.
To find out which of the many possible BNCs works the best, many different BNCs have to be fitted to the data.
Therefore it is important that fitting a single BNC is performed as efficiently as possible.
In this project, a new algorithm for learning BNCs from data has been developed and implemented,
and its efficiency has been compared experimentally to existing algorithms for performing this task.
This comparison was performed by applying the algorithms to benchmark data sets from the UCI machine learning repository, and to artificially generated data.
The results showed that the newly developed algorithm is, in many cases, more efficient than its competitors,
in particular when the BNC to be fitted has a relatively simple structure.
|Special Note||This project has resulted in the publication "Discriminative Learning of Bayesian
Network Classifiers: a comparative study",
to be published in the proceedings of the Third International Workshop on Probabilistic Graphical Models (PGM'06).