Instance-Based Learning Algorithms
Machine Learning
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Rule Sets from Model Trees
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A feature selection technique for classificatory analysis
Pattern Recognition Letters
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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The changes occurring in the dynamics of sugar concentration in grape berries are fairly significant during maturation, whereby they are commonly used as a marker of their development. In view of the importance this parameter has for wine producers, this paper designs several models for predicting the must's probable alcohol level using both meteorological variables and those specific to the vineyard. Presentation is made of a comparative analysis of learning and meta-learning algorithms for the selection of variables and the design of useful predictive models for estimating this level. The models are designed according to data gathered at different locations within the Rioja Qualified Designation of Origin (DOC Rioja, Spain) under different climate conditions, as well as involving different grape varieties. The models designed in this study provide very good results, and following their validation by experts, they have been proven to make a major contribution to decision-making in vine growing. Finally, considering the indices of analysis studied, it has been observed that the ensemble-type model based on the Bagging algorithm with REPTree decision trees records the best results, with a root mean squared error (RMSE) of 8.1% and a correlation of 84.9%.