Heterogeneity of synovial molecular patterns in patients with arthritis.

Objectives: Early diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.

A double pruning scheme for boosting ensembles.

Ensemble learning consists of generating a collection of classifiers whose predictions are then combined to yield a single unified decision. Ensembles of complementary classifiers provide accurate and robust predictions, which are often better than the predictions of the individual classifiers in the ensemble. Nevertheless, ensembles also have some drawbacks: typically, all classifiers are queried to compute the final ensemble prediction.

Statistical instance-based pruning in ensembles of independent classifiers.

The global prediction of a homogeneous ensemble of classifiers generated in independent applications of a randomized learning algorithm on a fixed training set is analyzed within a Bayesian framework. Assuming that majority voting is used, it is possible to estimate with a given confidence level the prediction of the complete ensemble by querying only a subset of classifiers. For a particular instance that needs to be classified, the polling of ensemble classifiers can be halted when the probability that the predicted class will not change when taking into account the remaining votes is above the specified confidence level.

An analysis of ensemble pruning techniques based on ordered aggregation.

Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble.