Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment

Cardoso, Jaime S.; Costa, Joaquim Pinto da; Cardoso, Maria João

doi:10.1016/j.neunet.2005.06.023

Cited by 54 publications

(47 citation statements)

References 24 publications

(43 reference statements)

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“…The problem of ordinal classification, also called ordinal regression in statistics, has received increasing attention in the machine learning field in recent years (Frank and Hall, 2001;Chu and Keerthi, 2005;Cardoso et al, 2005;Yu et al, 2006;Cardoso and da Costa, 2007;Babaria et al, 2007). In ordinal classification, the set of class labels Figure 1: On the left, the distribution of classes in the instance space is in well agreement with the class order y 1 ≺ y 2 ≺ y 3 ≺ y 4 , while this is not the case in the right situation.…”

Section: Introductionmentioning

confidence: 99%

Is an ordinal class structure useful in classifier learning?

Hühn

Hüllermeier

2008

IJDMMM

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In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information, and which properties of these techniques are important in this regard.

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Section: Introductionmentioning

confidence: 99%

Is an ordinal class structure useful in classifier learning?

Hühn

Hüllermeier

2008

IJDMMM

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“…Pattern recognition problems of classifying examples into ordered classes (ordinal classification), have received a great deal of attention as they appear in many practical applications (e.g. information retrieval [15], astronomical analysis [14] and medical applications [16]). Recently, the Generalized Matrix LVQ (GMLVQ) [4] was extended to the Ordinal GMLVQ (OGMLVQ) [2], that is specifically designed for classifying data items into ordered classes.…”

Section: A Prototype-based Models and Their Ordinal Extensionmentioning

confidence: 99%

Ordinal-based metric learning for learning using privileged information

Fouad

Tiňo

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Learning Using privileged Information (LUPI), originally proposed in [1], is an advanced learning paradigm that aims to improve the supervised learning in the presence of additional (privileged) information, available during training, but not in the test phase. We present a novel metric learning methodology that is specially designed for incorporating privileged information in ordinal classification tasks, where there is a natural order on the set of classes. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. The proposed model is formulated in the context of ordinal prototype based classification with metric adaptation. Unlike the existing nominal version of LUPI in prototype models [8], [9], in ordinal classifications the proposed LUPI model takes explicitly into account the class order information during the input space metric learning. Experiments demonstrate that incorporating privileged information via the proposed ordinal-based metric learning can improve the ordinal classification performance.

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“…Pattern recognition problems of classifying examples into ordered classes, namely ordinal classifications, have received a great attention in the recent literatures. They lend themselves to many practical applications as in (Chu et al, 2007;Cardoso et al, 2005). In this paper we would like to extent the LVQ framework to ordinal classification, since the existing LVQ models do not consider the ordinal label information explicitly during learning.…”

Section: Generalized Mlvq (Gmlvq)mentioning

confidence: 99%

“…However, many pattern recognition problems involve classifying data into classes which have a natural ordering. This type of problem, known as ordinal classification or ordinal regression, is commonly seen in several real life applications, as in information retrieval (Chu et al, 2007) and medical analysis (Cardoso et al, 2005). In such problems, although it is still possible to use the conventional (nominal) methods, the order relation among the classes will be ignored, which may affect the stability of learning and the overall prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Metric Learning Vector Quantization for Ordinal Classification

Fouad

Tiňo

2012

Neural Computation

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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 two novel ordinal Learning Vector Quantization (LVQ) schemes, 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 formulations compare favourably with their nominal counterparts.Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.

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Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment

Cited by 54 publications

References 24 publications

Is an ordinal class structure useful in classifier learning?

Is an ordinal class structure useful in classifier learning?

Ordinal-based metric learning for learning using privileged information

Adaptive Metric Learning Vector Quantization for Ordinal Classification

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