Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Machine Learning
Neural Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Two algorithms for generating structured and unstructured monotone ordinal data sets
Engineering Applications of Artificial Intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
Learning to predict ice accretion on electric power lines
Engineering Applications of Artificial Intelligence
Precipitation forecasting by using wavelet-support vector machine conjunction model
Engineering Applications of Artificial Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Exploitation of pairwise class distances for ordinal classification
Neural Computation
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Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are not direct wind measures available. Different approaches have been applied to this reconstruction, such as measure-correlate-predict algorithms, approaches based on physical models such as reanalysis methods, or more recently, indirect measures such as pressure, and its relation to wind speed. This paper adopts the latter method, and deals with wind speed estimation in wind farms from pressure measures, but including different novelties in the problem treatment. Existing synoptic pressure-based indirect approaches for wind speed estimation are based on considering the wind speed as a continuous target variable, estimating then the corresponding wind series of continuous values. However, the exact wind speed is not always needed by wind farm managers, and a general idea of the level of speed is, in the majority of cases, enough to set functional operations for the farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classification task, given that the problem is simplified. Thus, this paper tackles the problem of wind speed prediction from synoptic pressure patterns by considering wind speed as a discrete variable and, consequently, wind speed prediction as a classification problem, with four wind level categories: low, moderate, high or very high. Moreover, taking into account that these four different classes are associated to four values in an ordinal scale, the problem can be considered as an ordinal regression problem. The performance of several ordinal and nominal classifiers and the improvement achieved by considering the ordering information are evaluated. The results obtained in this paper present the support vector machine as the best tested classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to significantly improve ranks with respect to nominal classification, although differences in accuracy aresmall.