Feature Selection Via Joint Likelihood

The field of feature selection has many different competing algorithms, selection criteria and measure functions, with little theoretical justification for the choice of one measure over another. This thesis focuses on feature selection algorithms that use information theoretic criteria and provide a solid theoretical justification for their use. It also presents experimental results showing how the different factorisation assumptions affect classification performance.