File(s) not publicly available

Neural network based classifier ensembles : a comparative analysis

chapter
posted on 06.12.2017, 00:00 by Brijesh VermaBrijesh Verma
This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare our recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. First type is a classifier ensemble that uses a neural network for all its base classifiers. Second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. Third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository.

History

Parent Title

Machine learning algorithms for problem solving in computational applications : intelligent techniques

Start Page

229

End Page

239

Number of Pages

11

ISBN-13

9781466618336

Publisher

IGI Global

Place of Publication

USA

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Number of Chapters

21