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Soft Computing
Erschienen in: Soft Computing 10/2015

01.10.2015 | Methodologies and Application

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

verfasst von: Theodoros Theodoridis, Huosheng Hu

Erschienen in: Soft Computing | Ausgabe 10/2015

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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.
Vorheriger Artikel A novel optimization algorithm inspired by the creative thinking process
Nächster Artikel Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks

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Fußnoten
1
The expression \(e_i \ne a_i\) denotes a misclassified instance.
 
2
The expression \(e_i = -1\) denotes an out-of-scope misclassified instance (\(e_i \notin C\)).
 
3
The original statistical functions derived from performance metrics, which have been used for the multiclass problem, are included in the work of Sokolova et al. (2006).
 
4
All primitive abbreviations can be found in the primitive table presented in Theodoridis and Hu (2008).
 
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Metadaten
Titel
The binomial-neighbour instance-based learner on a multiclass performance measure scheme
verfasst von
Theodoros Theodoridis
Huosheng Hu
Publikationsdatum
01.10.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 10/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1461-z

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