Ensemble classifier refers to a group of individual classifiers that are cooperatively trained on data set in a supervised classification problem. In this paper we present a review of commonly used ensemble classifiers in the literature. Some ensemble classifiers are also developed targeting specific applications. We also present some application driven ensemble classifiers in this paper.

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different training signals for base networks in an ensemble c...

A number of ensemble algorithms for solving multi-label classification problems have been proposed in recent years. Diversity among the base learners is known to be important for constructing a good ensemble. In this paper we define a method for introducing diversity among the base learners of one of the previously presented multi-label ensemble classifiers. An empirical comparison on 10 datase...

We propose a novel stream data classification technique to detect Peer to Peer botnet. Botnet traffic can be considered as stream data having two important properties: infinite length and drifting concept. Thus, stream data classification technique is more appealing to botnet detection than simple classification technique. However, no other botnet detection approaches so far have applied stream...

Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based...

Classification is one of the critical task in datamining. Many classifiers exist for classification task and each have their own pros and cons. It is observed that due to imbalancing in datasets quality of classification accuracy is decreasing. Thus the increasing rate of data diversity and size decreases the performance and efficiency of classifiers. Thus it is very much important to get the m...

the area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, ...

In many practical classification problems, mislabeled data instances (i.e., class noise) exist in the acquired (training) data and often have a detrimental effect on the classification performance. Identifying such noisy instances and removing them from training data can significantly improve the trained classifiers. One such effective noise detector is the so-called ensemble filter, which pred...

This article presents a comprehensive study of different ensemble pruning techniques applied to a bagging ensemble composed of decision stumps. Six different ensemble pruning methods are tested. Four of these are greedy strategies based on first reordering the elements of the ensemble according to some rule that takes into account the complementarity of the predictors with respect to the classi...

This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching s...