Ensemble methods such as bagging and boosting have been successfully applied to classification problems. Two important issues associated with an ensemble approach are: how to generate models to construct an ensemble, and how to combine them for classification. In this paper, we focus on the problem of model generation for heterogeneous data classification. If we could partition heterogeneous da...

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain–computer in...

Knowledge discovery from infinite data streams is an important and difficult task.We are facing two challenges, the overwhelming volume and the concept drifts of the streaming data. In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Bayesian, et...

Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows the identification of subensembles that require less memory for storage, have a faster classification speed and can perform better than the original bagging ensemble. Furthermore, ensemble pruning does not ...

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 ...

Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approac...

In this study we introduce a neural network ensemble composed of several linear perceptrons, to be used as a classifier that can rapidly be trained and effectively deals with nonlinear problems. Although each member of the ensemble can only deal with linear classification problems, through a competitive training mechanism, the ensemble is able to automatically allocate a part of the learning sp...

Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to...