If classifiers are selected from a hypothesis class to form an ensemble, bounds on average error rate over the selected classifiers include a component for selectivity, which grows as the fraction of hypothesis classifiers selected for the ensemble shrinks, and a component for variety, which grows with the size of the hypothesis class or in-sample data set. We show that the component for select...

An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances. Early research has proved that ensemble classifiers in most cases can be more accurate than any single component classifier both empirically and theoretically. Though many ensemble approaches are proposed, it is still not an e...

We propose a novel ensemble learning algorithm called Triskel, which has two interesting features. First, Triskel learns an ensemble of classifiers that are biased to have high precision (as opposed to, for example, boosting, where the ensemble members are biased to ignore portions of the instance space). Second, Triskel uses weighted voting like most ensemble methods, but the weights are assig...

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier’s classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble ...

We present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital, when reliable acquisition of vital-sign data may be difficult. The decision tool uses basic vital-sign variables as input into linear classifiers, which are then combined into an ensemble classifier. The classifier identifies hypovolemic patient...

A classifier ensemble is a group of individual base classifiers. Each classifier is trained individually by modifying the given data set to achieve diversity. During the testing phase the results given by each classifier are collected to give the final result using a technique called as majority voting. Empirical results prove that diversity amongst the base classifiers improves the accuracy of...

The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set mwor = n. Without-replacement methods typically use half samples mwr = n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of...

We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We also construct ensembles of classifiers learned from such actively sampled image sets, which further provides improvement in the recognition rates. We not only significantly reduce the number of images required in the...

One of the most important directions in improvement of the datamining and knowledge discovery methods is the integration of the multiple classification techniques based on ensembles of classifiers. An integration technique should solve the problem of estimation and selection of the most appropriate component classifiers for an ensemble. We discuss an advanced dynamic integration of multiple cla...

We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classific...