Ordinal regression problems are those machine learning problems where the objective is to
classify patterns using a categorical scale which shows a natural order between the labels …

In this paper, the performance of different ordinal and nominal multi-class classifiers is
evaluated, in a problem of wave energy range prediction using meteorological variables …

The classification of patterns into naturally ordered labels is referred to as ordinal regression
or ordinal classification. Usually, this classification setting is by nature highly imbalanced …

In this paper, the well known stagewise additive modeling using a multiclass exponential
(SAMME) boosting algorithm is extended to address problems where there exists a natural …

Liver transplantation is nowadays a widely-accepted treatment for patients who present a
terminal liver disease. Nevertheless, transplantation is greatly hampered by the un …

The classification of patterns into naturally ordered labels is referred to as ordinal
regression. This paper proposes an ensemble methodology specifically adapted to this type …

Ordinal regression (OR) is an important branch of supervised learning in between the
multiclass classification and regression. In this paper, the traditional classification scheme of …

Sustainable development (SD) is a major challenge for nations, even more so in the current
economic crisis and uncertain environment. Although different indicators, compindices and …

In this paper we propose a novel computational system for simultaneous modelling and
prediction of rainfall occurrence and amount. The proposed system is based on a …

Designing a classifier in the absence of labeled data is becoming a common encounter as
the acquisition of informative labels is often difficult or expensive, particularly on new …