The imprecise Dirichlet model as a basis for a new boosting classification algorithm
A new algorithm for ensemble construction based on adapted restricting a set of weights of examples in training data to avoid overfitting and to reduce a number of iterations is proposed in the paper. The algorithm called IDMBoost (Imprecise Dirichlet Model Boost) applies Walley?s imprecise Dirichlet model for modifying the restricted sets of weights depending on the number and location of classification errors. Updating of weights within the restricted set (simplex) is carried out by using its extreme points. The proposed algorithm has a double adaptation procedure. The first adaptation is carried out within every restricted simplex like the AdaBoost. The second adaptation reduces and changes the restricted sets of possible weights of examples. Various numerical experiments with real data illustrate the proposed algorithm.
imprecise dirichlet model boosting classification extreme points adaboost machine learning