Many methods have been proposed for combining multiple classifiers in pattern recognition such as Random Forest which uses decision trees for problem solving. In this paper, we propose a weighted vote-based classifier ensemble method. The proposed method is similar to Random Forest method in employing many decision trees and neural networks as classifiers. For evaluating the proposed weighting ...

In multi-label learning, the relationship among labels is well accepted to be important, and various methods have been proposed to exploit label relationships. Amongst them, ensemble of classifier chains (ECC) which builds multiple chaining classifiers by random label orders has drawn much attention. However, the ensembles generated by ECC are often unnecessarily large, leading to extra high co...

The techniques of classification are through learning historical data to help people to predict the class label of data, and they have been applied to solve many problems. In the real world, there exists many sequence data, such as genome sequences, those should be learned and analyzed for predicting class labels. The traditional classification methods are unsuitable for sequence data. This stu...

To improve the accuracy of data classification systems, several techniques using classifier fusion have been suggested. This paper proposed a model of classifier fusion for character recognition problem. The work presented here aims to tackle the disadvantages and benefit of different classifiers with varying feature sets. In particular, this approach proposes the use of statistical procedures ...

In this paper, we propose a new research problem on active learning from data streams where data volumes grow continuously. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. We propose a classifier-ensemble based active learning framework which selectively labels instances from data streams to build an e...

Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named Sealed is p...

Machine learning classifiers are a vital component of modern malware and intrusion detection systems. However, past studies have shown that classifier based detection systems are susceptible to evasion attacks in practice. Improving the evasion resistance of learning based systems is an open problem. To address this, we introduce a novel method for identifying the observations on which an ensem...

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i...

1BAbstract This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was to identify the grazed areas from Quickbird imagery to map their spatial extent. Seve...