BACKGROUND We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. METHODS We employed the National Inpatie...

A new boosting algorithm of Freund and Schapire is used to improve the performance of an ensemble of decision trees which are constructed using the information ratio criterion of Quinlan’s C4.5 algorithm. This boosting algorithm iteratively constructs a series of decision trees, each decision tree being trained and pruned on examples that have been filtered by previously trained trees. Examples...

This note presents a chronological review of the literature on ensemble learning which has accumulated over the past twenty years. The idea of ensemble learning is to employ multiple learners and combine their predictions. If we have a committee of M models with uncorrelated errors, simply by averaging them the average error of a model can be reduced by a factor of M. Unfortunately, the key ass...

We present an extensive empirical comparison between twenty prototypical supervised ensemble learning algorithms, including Boosting, Bagging, Random Forests, Rotation Forests, Arc-X4, Class-Switching and their variants, as well as more recent techniques like Random Patches. These algorithms were compared against each other in terms of threshold, ranking/ordering and probability metrics over ni...

W e study the effectiveness of three neural network ensembles in improving OCR performance: ( i ) Basic, (ii) Bagging, and (iii) Boosting. Three random character degradation models are introduced in training indivadual networks in order to reduce error correlation between individual networks and to improve the generalization ability of neural networks. We compare the recognition accuracies of t...

We present an application of the ensemble learning algorithm in the area of visual tracking and servoing. In particular, we investigate an approach based on the Boosting technique for robust visual tracking of color objects in an underwater environment. To this end, we use AdaBoost, the most common variant of the Boosting algorithm, to select a number of low-complexity but moderately accurate c...

In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...