C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Hierarchical classification: combining Bayes with SVM
ICML '06 Proceedings of the 23rd international conference on Machine learning
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Predicting vertical acceleration of railway wagons using regression algorithms
IEEE Transactions on Intelligent Transportation Systems
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Railroad wheel inspection attempts to identify failing wheels from a large population of wheels in service. This is a critical yet time consuming task. This paper presents a machine learning approach to automate the identification process using collected data from wheel inspection. Decision tree based and support vector machine based classification methods have been applied to the wheel inspection data analysis. A variation of the bagging ensemble approach is developed to improve the classification accuracy. The results of these methods achieve an identification accuracy of 80%. Analysis of the rules and models derived, as well as comparisons of the classification results obtained using the two base classification approaches are presented.