Neural Comput - Adaptive metric learning vector quantization for ordinal classification.

Tópicos

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Resumo

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 a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.

Resumo Limpo

mani pattern analysi problem requir classif exampl natur order class case nomin classif scheme will ignor class order relationship can detriment effect classif accuraci articl introduc two novel ordin learn vector quantiz lvq scheme metric learn specif design classifi data item order class ordin lvq unlik nomin lvq class order inform use train select class prototyp adapt well determin exact manner prototyp get updat prototypebas model general amen interpret can often construct smaller comput cost altern nonlinear classif model experi demonstr propos ordin lvq formul compar favor nomin counterpart moreov method achiev competit perform exist benchmark ordin regress model

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