In this paper, a new algorithm called Averaged Conservative Boosting (ACB) is presented to build ensembles of neural networks. In ACB we mix the improvements that Averaged Boosting (Aveboost) and Conservative Boosting (Conserboost) made to Adaptive Boosting (Adaboost). In the algorithm we propose we have applied the conservative equation used in Conserboost along with the averaged procedure use...

We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning tasks whose solutions use a boosting technique. The boosting approach was originally suggested for the standard PAC model; we analyze possible applications of boosting in the context of agnostic learning, which is more ...

This editorial is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" and "Extending Statistical Boosting - An Overview of Recent Methodological Developments", written by Andreas Mayr and co-authors. It preludes two discussed reviews on developments and applications of boo...

Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of regression models using the same learning algorithm as base-learner. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in t...

Boosting methods combine a set of moderately accurate weak learners to form a highly accurate predictor. Despite the practical importance of multi-class boosting, it has received far less attention than its binary counterpart. In this work, we propose a fully-corrective multi-class boosting formulation which directly solves the multi-class problem without dividing it into multiple binary classi...

In previous researches it can been seen that Bagging, Boosting and Cross-Validation Committee can provide good performance separately. In this paper, Boosting methods are mixed with Bagging and Cross-Validation Committee in order to generate accurate ensembles and take benefit from all these alternatives. In this way, the networks are trained according to the boosting methods but the specific t...

This paper presents a novel way to speed up the classification time of a boosting classifier. We make the shallow (flat) network deep (hierarchical) by growing a tree from the decision regions of a given boosting classifier. This provides many short paths for speeding up and preserves the Boosting decision regions, which are reasonably smooth for good generalisation. We express the conversion a...

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...