Projects per year
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.
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Number of pages||8|
|Publication status||Published - 2012|
|Event||13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012 - Natal, Brazil|
Duration: 29 Aug 2012 → 31 Aug 2012
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012|
|Period||29/08/12 → 31/08/12|
- Matrix Learning Vector Quantization (MLVQ)
- Ordinal Classification
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
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- 1 Finished
Tino, P. & Kourtzi, Z.
1/10/10 → 30/03/14
Project: Research Councils