Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables. If ordinal variables are discrete realizations of unmeasured conti...

Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a fixed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the highdimensional feat...

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-param...

Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to mo...

We show that classification rules used in ordinal regression are equivalent to a certain class of linear multi-class classifiers. This observation not only allows to design new learning algorithms for ordinal regression using existing methods for multi-class classification but it also allows to derive new models for ordinal regression. For example, one can convert learning of ordinal classifier...

To design successful new products and services, managers need to measure consumer preferences relative to product attributes. Many existing methods use ordinal measures. Intensity measures have the potential to provide more information per question, thus allowing more accurate models or fewer consumer questions (lower survey cost, less consumer wearout). To exploit this potential, researchers m...

Based on the Enterprise Architecture Value Framework (EAVF) a generic framework to classify benefits of Enterprise Architecture (EA) a measurement instrument for EA benefits has been developed and tested in a survey with 287 respondents. In this paper we present the results of this survey in which stakeholders of EA were questioned about the kind of benefits they experience from EA in their org...

We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranking rule from the binary classifier. A weighted 0/1 loss of the binary c...

background: breast cancer is the most prevalent malignancy in women and there are many possible reasons for its occurrence. one important issue in different kinds of cancers is the spreading of cancerous cells to other tissues (metastasis). therefore, we have investigated the factors associated with the prediction of metastasis of breast cancer with data mining tools. methods: this study is a c...