Tree ensemble or combining methods that use re-sampling technique have been highlighted recently in Statistical classification and Data mining. In this paper, we propose a new ensemble method in decision trees that utilizes systematic patterns of classification. The new method improved the prediction accuracy of a single decision tree algorithm. It is also observed that this method performs rea...

Bayesian and decision tree classifiers are among the most popular classifiers used in the data mining community and recently numerous researchers have examined their sufficiency in ensembles. Although, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best. In this work, we propose Bagged Voting using different subsets of the same training...

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The i...

Ensemble learning schemes have shown impressive increases in prediction accuracy over single model schemes. We introduce a new decision forest learning scheme, whose base learners are Minimum Message Length (MML) oblique decision trees. Unlike other tree inference algorithms,MMLoblique decision tree learning does not over-grow the inferred trees. The resultant trees thus tend to be shallow and ...

The article shortly discusses the aim of classification task and its application to different domains of life. The idea of ensemble of classifiers is presented and some aspects of grouping methods are discussed. The paper points to the need of ensemble classifier pruning and presents a new approach for ensemble reduction. The proposed method is dedicated to committees of decision trees and base...

Linear regression and regression tree models are among the most known regression models used in the machine learning community and recently many researchers have examined their sufficiency in ensembles. Although many methods of ensemble design have been proposed, there is as yet no obvious picture of which method is best. One notable successful adoption of ensemble learning is the distributed s...

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is th...

This paper introduces a novel splitting criterion parametrized by a scalar ‘α’ to build a class-imbalance resistant ensemble of decision trees. The proposed splitting criterion generalizes information gain in C4.5, and its extended form encompasses Gini(CART) and DKM splitting criteria as well. Each decision tree in the ensemble is based on a different splitting criterion enforced by a distinct...

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies, which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest nei...