caa a22 4500 605469741 CHVBK 20210128100324.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00500-014-1461-z doi (NATIONALLICENCE)springer-10.1007/s00500-014-1461-z The binomial-neighbour instance-based learner on a multiclass performance measure scheme [Elektronische Daten] [Theodoros Theodoridis, Huosheng Hu] 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. Springer-Verlag Berlin Heidelberg, 2014 Instance-based learning nationallicence k-Nearest neighbours nationallicence Lazy learners nationallicence Action recognition nationallicence Theodoridis Theodoros University of Salford, Salford, UK aut Hu Huosheng University of Essex, Colchester, UK aut Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2973-2981 1432-7643 19:10<2973 2015 19 500 https://doi.org/10.1007/s00500-014-1461-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1461-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Theodoridis Theodoros University of Salford, Salford, UK aut NATIONALLICENCE 700 1- Hu Huosheng University of Essex, Colchester, UK aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2973-2981 1432-7643 19:10<2973 2015 19 500