Theodoridis, T and Hu, H 2015, 'The binomial-neighbour instance-based learner on a multiclass performance measure scheme' , Soft Computing, 19 (10) , pp. 2973-2981.
Full text not available from this repository.Abstract
This paper presents a novel instance-based learning methodology the Binomial-Neighbour (B-N) algorithm. Unlike to other k-Nearest Neighbour algorithms, B-N employs binomial search through vectors of statistical features and distance primitives. The binomial combinations derived from the search with best classification accuracy are distinct primitives which characterise a pattern. The statistical features employ a twofold role; initially to model the data set in a dimensionality reduction preprocessing, and finally to exploit these attributes to recognise patterns. The paper introduces as well a performance measure scheme for multiclass problems using type error statistics. We harness this scheme to evaluate the B-N model on a benchmark human action dataset of normal and aggressive activities. Classification results are being compared with the standard IBk and IB1 models achieving significantly exceptional recognition performance.
| Item Type: | Article |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering |
| Journal or Publication Title: | Soft Computing |
| Publisher: | Springer |
| ISSN: | 1432-7643 |
| Depositing User: | WM Taylor |
| Date Deposited: | 08 Nov 2016 09:53 |
| Last Modified: | 12 Nov 2018 12:26 |
| URI: | http://usir.salford.ac.uk/id/eprint/40659 |
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