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The binomial-neighbour instance-based learner on a multiclass performance measure scheme

Theodoridis, T and Hu, H (2015) 'The binomial-neighbour instance-based learner on a multiclass performance measure scheme.' Soft Computing, 19 (10). 2973 - 2981. ISSN 1432-7643

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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
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 21 Jul 2015 15:23
Last Modified: 30 Mar 2021 23:15

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