TY - GEN
T1 - Prototype based modelling for ordinal classification
AU - Fouad, Shereen
AU - Tino, Peter
PY - 2012
Y1 - 2012
N2 - Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases nominal classification schemes will ignore the class order relationships, which can have detrimental effect on classification accuracy. This paper introduces a novel ordinal Learning Vector Quantization (LVQ) scheme, with metric learning, specifically designed for classifying data items into ordered classes. Unlike in nominal LVQ, in ordinal LVQ the class order information is utilized during training in selection of the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype based models are in general more amenable to interpretations and can often be constructed at a smaller computational cost than alternative non-linear classification models. Experiments demonstrate that the proposed ordinal LVQ formulation compares favorably with its nominal counterpart. Moreover, our method achieves competitive performance against existing benchmark ordinal regression models.
AB - Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases nominal classification schemes will ignore the class order relationships, which can have detrimental effect on classification accuracy. This paper introduces a novel ordinal Learning Vector Quantization (LVQ) scheme, with metric learning, specifically designed for classifying data items into ordered classes. Unlike in nominal LVQ, in ordinal LVQ the class order information is utilized during training in selection of the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype based models are in general more amenable to interpretations and can often be constructed at a smaller computational cost than alternative non-linear classification models. Experiments demonstrate that the proposed ordinal LVQ formulation compares favorably with its nominal counterpart. Moreover, our method achieves competitive performance against existing benchmark ordinal regression models.
KW - Matrix Learning Vector Quantization (MLVQ)
KW - Ordinal Classification
UR - http://www.scopus.com/inward/record.url?scp=84865024703&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-642-32639-4_26
U2 - 10.1007/978-3-642-32639-4_26
DO - 10.1007/978-3-642-32639-4_26
M3 - Conference publication
AN - SCOPUS:84865024703
SN - 9783642326387
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 208
EP - 215
BT - Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings
PB - Springer
T2 - 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012
Y2 - 29 August 2012 through 31 August 2012
ER -