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

Theodoridis, T; Hu, H

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.

Citation

Theodoridis, T., & Hu, H. (2015). The binomial-neighbour instance-based learner on a multiclass performance measure scheme. Soft Computing, 19(10), 2973-2981. https://doi.org/10.1007/s00500-014-1461-z

• Journal Article Type: Article
• Online Publication Date: Sep 17, 2014
• Publication Date: Oct 1, 2015
• Deposit Date: Nov 8, 2016
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Publisher: Springer Verlag
• Volume: 19
• Issue: 10
• Pages: 2973-2981
• DOI: https://doi.org/10.1007/s00500-014-1461-z
• Publisher URL: http://dx.doi.org/10.1007/s00500-014-1461-z