Theodoridis, T 
ORCID: https://orcid.org/0000-0001-7482-318X and Hu, H
2015,
'The binomial-neighbour instance-based learner on a multiclass performance measure scheme'
, Soft Computing, 19 (10)
, pp. 2973-2981.
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: | USIR Admin |
| Date Deposited: | 08 Nov 2016 09:53 |
| Last Modified: | 27 Aug 2021 20:33 |
| URI: | https://usir.salford.ac.uk/id/eprint/40659 |
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