Tree-Based Ensemble Models and Algorithms for Classification

An ensemble method is viewed as a compound model. The purpose of such a model is to achieve better predictive performance. The attempt is to tune predictions to observations by decreasing model variance, and bias. First the work focuses at the presentation of the Projective Decision Tree Algorithm (PA), a sort of Decision Tree (DT) based on purity and using the criterion of next node (CNN). Secondly, two sets of algorithms that provide improvement of the predictive performance are developed the first set of the Tree-Based Ensemble models of bagging and boosting types and the second set of known individual algorithms. The accuracy performance of the two sets with comparison is examined. Promising results based on accuracy of the proposed models are obtained.