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...

Diversity is a key characteristic to obtain advantages of combining predictors. In this paper, we propose a modification of bagging to explicitly trade off diversity and individual accuracy. The procedure consists in dividing the bootstrap replicates obtained at each iteration of the algorithm in two subsets: one consisting of the examples misclassified by the ensemble obtained at the previous ...

We propose a mutual learning with a latent teacher within the framework of on-line learning, and have analyzed its dynamical behavior through the statistical mechanics method. The proposed model consists of two learning steps: two students independently learn from a teacher, and then the students learn from each other through the mutual learning. A teacher is not used in the mutual learning, so...

Machine learning has become a powerful approach in practical applications, such as decision making, sentiment analysis and ontology engineering. To improve the overall performance in machine learning tasks, ensemble learning has become increasingly popular by combining different learning algorithms or models. Popular approaches of ensemble learning include Bagging and Boosting, which involve vo...

The use of ensemble of classifiers for classification of medical data derived from diagnostic devices has been proposed in this research. The experimental studies were carried out on three datasets concerning different medical problems: arrhythmia, breast cancer and coronary artery disease using SPECT images. The comparison of single classification algorithms (kNNIBk, C4.5 J48, Naïve Bayes, Ran...

In this paper, we propose to use classifier ensemble (CE) as a method to enhance the robustness of machine learning (ML) kernels in presence of hardware error. Different ensemble methods (Bagging and Adaboost) are explored with decision tree (C4.5) and artificial neural network (ANN) as base classifiers. Simulation results show that ANN is inherently tolerant to hardware errors with up to 10% h...

Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken ...