Dmitry Pidan, M.Sc. Thesis Seminar
Wednesday, 13.7.2011, 13:00
Focusing on short term trend prediction in a financial context we consider the problem of selective prediction whereby
the predictor can abstain from prediction in order to improve its performance. The main characteristic of selective
predictors is the trade-off they exhibit between error and coverage rates. In the context of classification selective
prediction is termed "classification with a reject option", and there the main idea for implementing rejection is Chow's ambiguity principle
In this talk we examine two types of selective HMM predictors. The first is an ambiguity-based rejection in the spirit of Chow. The second
is a specialized mechanism for HMMs that identifies low quality HMM states and abstain from prediction in those states.
We call this model selective HMM (sHMM). In both approaches we can trade-off prediction coverage to gain better accuracy in a controlled manner.
We compare the performance of ambiguity-based HMM rejection technique to that of the sHMM approach, demonstrate the effectiveness of both methods
and the superiority of the sHMM model.