Al-Jubouri, Bassma ORCID: https://orcid.org/0000-0001-7509-1478 and Gabrys, Bogdan (2017) Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach. In: Cong, G., Peng, W. C., Zhang, W., Li, C. and Sun, A., (eds.) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science (10604). Springer International Publishing

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Abstract

This paper discusses the effect of locality and diversity among the base models of a Multi-Components Multi-Layer Predictive System (MCMLPS). A new ensemble method is introduced, where in the proposed architecture, the data instances are assigned to local regions using a conditional mutual information based on the similarity of their features. Furthermore, the outputs of the base models are weighted by this similarity metric. The proposed architecture has been tested on a number of data sets and its performance was compared to four bench- mark algorithms. Moreover, the effect of changing three parameters of the proposed architecture has been tested and compared.

Item Type:	Book Section
Status:	Published
DOI:	10.1007/978-3-319-69179-4_22
Subjects:	Q Science > Q Science (General) > Q325 Machine learning Q Science > QA Mathematics > QA75 Electronic computers. Computer science
School/Department:	York Business School
URI:	https://ray.yorksj.ac.uk/id/eprint/15976

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