An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are available, it is better to ensemble some instead of all of them. The selection, however, is generally difficult and heuristics are often used. In this paper, we investigate the problem under the regularization framework, and...

In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logis...

Abstract: Frequency prediction after a disturbance has received increasing research attention given its substantial value in providing a decision-making foundation in power system emergency control. With the advancing development of machine learning, analysis power systems with machine-learning methods has become completely different from traditional approaches. In this paper, an ensemble algor...

The design of an ensemble guarantees success, only when its base classifiers make both limited errors as well as high accuracy. We address the problem of achieving the possible enhancement of accuracy of the learning models for diabetes data set.In this paper we design an ensemble by four types of Meta level classifiers for this purpose. The base classifiers for feeding the ensemble we have pro...

Ensemble methods have become very well known for being powerful pattern recognition algorithms capable of achieving high accuracy. However, Ensemble methods produces learners that are not comprehensible or transferable thus making them unsuitable for tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensibl...

This paper presents an approach with ensemble classifiers using unsupervised data selection for speaker recognition. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. Based on its acoustic characteristics, the speech utterance is divided into several subsets using unsupervised data select...

Ensemble models can achieve more accurate predictions than single learners. Selective ensembles further improve the predictions by selecting an informative subset of the full ensemble. We consider reinforcement learning ensembles, where the members are neural networks. In this context we study a new algorithm for ensemble subset selection in reinforcement learning scenarios. The aim of the prop...