The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust w...

The performance of a single weak classifier can be improved by using combining techniques such as bagging, boosting and the random subspace method. When applying them to linear discriminant analysis, it appears that they are useful in different situations. Their performance is strongly affected by the choice of the base classifier and the training sample size. As well, their usefulness depends ...

Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the first classifier is an unpruned tree from a 10% sample of the training data. Grafting is a method for constructing ensembles of decision trees, where each tree is a grafted tree. Grafting by itself is better than Baggin...

Ensemble models—built by methods such as bagging, boosting, and Bayesian model averaging—appear dauntingly complex, yet tend to strongly outperform their component models on new data. Doesn’t this violate “Occam’s razor”—the widespread belief that “the simpler of competing alternatives is preferred”? We argue no: if complexity is measured by function rather than form—for example, according to g...

Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorit...

In this paper a novel ensemble based techniques for face recognition is presented. In ensemble learning a group of methods are employed and their results are combined to form the final results of the system. Gaining the higher accuracy rate is the main advantage of this system. Two of the most successful wrapping classification methods are bagging and boosting. In this paper we used the K neare...

The motivation for this study was to learn to predict forest fires in Slovenia using different data mining techniques. We used predictive models based on data from a GIS (geographical information system), the weather prediction model Aladin and MODIS satellite data. We examined three different datasets: one only for the Kras region, one for whole Primorska region and one for continental Sloveni...