Multistep sequential exploration of growing Bayesian classification models
C. Paass, Jörg Kindermann · 2000
If the collection of training data is costly, one can gain by actively selecting particular informative data points in a sequential way. In a Bayesian decision theoretic framework we develop a query selection criterion for classification models which explicitly takes into account the utility of decisions. We determine the overall utility and its derivative with respect to changes of the queries. An optimal query now may be obtained by stochastic hill climbing. Simultaneously, the model structure can be adapted by reversible jump Markov chain Monte Carlo.