We propose Twin Boosting which has much better feature selection behavior than boosting, particularly with respect to reducing the number of false positives (falsely selected features). In addition, for cases with a few important effective and many noise features, Twin Boosting also substantially improves the predictive accuracy of boosting. Twin Boosting is as general and generic as boosting. ...

To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are used as combining rules in bagging and boosting. However, other combining rules such as mean, product and average are possible. In this paper, we study bagging and boosting in Linear Discriminant Analysis (LDA) and t...

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules – also called base hypotheses. The so-called boosting algorithms iteratively find...

Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suuer from overrtting. Some examples of recent applications of boosting are also described.

This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semisupervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. T...

results before boosting, among 176 boosted individuals, 75 (42.6%) had hbsab 10 iu/l and were considered seroprotected. among 101 serosusceptible persons, more than 80% of boosted individuals showed more than twofold increase in cytokines concentration, which meant positive hbsag-specific cell-mediated immunity. mcl of both cytokines after boosting in giv were decreased more than twofold, possi...

Bagging and boosting are two general techniques for building predictors based on small samples from a dataset. We show that boosting can be parallelized, and then present performance results for parallelized bagging and boosting using OC1 decision trees and two standard datasets. The main results are that sample sizes limit achievable accuracy, regardless of computational time spent; that paral...

This paper presents a variant of the AdaBoost algorithm for boosting Näıve Bayes text classifier, called AdaBUS, which combines active learning with boosting algorithm. Boosting has been evaluated to effectively improve the accuracy of machine-learning based classifiers. However, Näıve Bayes classifier, which is remarkably successful in practice for text classification problems, is known not to...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extension...

Motivated by results in information-theory, we describe a modification of the popular boosting algorithm AdaBoost and assess its performance both theoretically and empirically. We provide theoretical and empirical evidence that the proposed boosting scheme will have lower training and testing error than the original (nonconfidence-rated) version of AdaBoost. Our modified boosting algorithm and ...