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When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is, however, far from solved and many different techniques for...
Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy...
The test set accuracy for ensembles of classifiers selected based on single measures of accuracy and diversity as well as combinations of such measures is investigated. It is found that by combining measures, a higher test set accuracy may be obtained than by using any single accuracy or diversity measure. It is further investigated whether a multi-criteria search for an ensemble that maximizes both...
The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets,...
Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialized techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the...
When designing ensembles, it is almost an axiom that the base classifiers must be diverse in order for the ensemble to generalize well. Unfortunately, there is no clear definition of the key term diversity, leading to several diversity measures and many, more or less ad hoc, methods for diversity creation in ensembles. In addition, no specific diversity measure has shown to have a high correlation...
Most predictive modeling in information fusion is performed using ensembles. When designing ensembles, the prevailing opinion is that base classifier diversity is vital for how well the ensemble will generalize to new observations. Unfortunately, the key term diversity is not uniquely defined, leading to several diversity measures and many methods for diversity creation. In addition, no specific diversity...
The main contribution of this paper is to suggest a novel technique for automatic ensemble design, maximizing accuracy. The technique proposed first trains a large number of classifiers (here neural networks) and then uses genetic algorithms to select the members of the final ensemble. The proposed method, when evaluated on 22 publicly available data sets, results in ensembles obtaining very high...
To determine the objective and subjective benefits of correcting astigmatism in contact lens wearers, 35 moderate astigmats were refit from spherical contact lenses to toric hydrogels (Focus Toric). Patients showed a significant improvement of more than one line with the Bailey-Lovie visual acuity chart compared with what they could see with their spherical lenses. Improvement was greatest for the...
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