C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Solving regression problems with rule-based ensemble classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
SBIA '96 Proceedings of the 13th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Novel ensemble methods for regression via classification problems
Expert Systems with Applications: An International Journal
Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach
Artificial Intelligence in Medicine
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Parkinson disease is a degenerative disorder of the central nervous system. In the present paper, we study the effectiveness of regression tree ensembles to predict the presence and severity of symptoms from speech datasets. This is a regression problem. Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to convert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we also study a recently developed RvC ensemble method for the prediction of Parkinson disease. Experimental results suggest that the RvC ensembles perform better than a single regression tree. Experiments also suggest that regression tree ensembles created using bagging procedure can be a useful tool for predicting Parkinson disease. The RvC ensembles and regression tree ensembles performed similarly on the dataset.