Although the Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm has achieved favorable classification performance, it often yields suboptimal solutions to the ensemble pruning problem, due to its limited exploration within the whole solution space, which inspires us with the development of a novel Ensemble Pruning algorithm based on Randomized Greedy Selective Strategy and Ballot (RGSS&B...

Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; Thus Classifier ensemble is an important approach to handle the drawback. If an automatic and fast method is obtained to approximate the accuracies of different classifiers on a typical dataset, the learning can be converted to an optimization problem and genetic...

This paper investigates a number of ensemble methods for improving the performance of phoneme classification for use in a speech recognition system. Two ensemble methods are described; boosting and mixtures of experts, both in isolation and in combination. Results are presented on two speech recognition databases: an isolated word database and a large vocabulary continuous speech database. Thes...

This working note summarizes our submission to the LifeCLEF 2014 Bird Task which combines the outputs from a Python and Matlab classification system. The features used for both systems include Mel-Frequency Cepstral Coefficients (MFCC), time-averaged spectrograms and the provided meta-data. The Python subsystem combines a large ensemble of different classifiers with different subsets of the fea...

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is th...

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms which also induces classification ru...

The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capt...

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applicatio...

Bayesian network (BN) classifiers use different structures and different training parameters which leads to diversity in classification decisions. This work empirically shows that building an ensemble of several fine-tuned BN classifiers increases the overall classification accuracy. The accuracy of the constituent classifiers can be achieved by fine-tuning each classifier and the diversity is ...

This paper introduces an original method for guaranteed estimation of the accuracy for an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smalle...