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
Density biased sampling: an improved method for data mining and clustering
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Hierarchical Classification of Web Pages Using Support Vector Machine
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Handling Class Imbalance Problems via Weighted BP Algorithm
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Using multiple ontologies in information extraction
Proceedings of the 18th ACM conference on Information and knowledge management
Diversity exploration and negative correlation learning on imbalanced data sets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Expert Systems with Applications: An International Journal
Predicting vertical acceleration of railway wagons using regression algorithms
IEEE Transactions on Intelligent Transportation Systems
Clustering based bagging algorithm on imbalanced data sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Dual support vector domain description for imbalanced classification
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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In many applications, data collected are highly skewed where data of one class clearly dominates data from the other classes. Most existing classification systems that perform well on balanced data give very poor performance on imbalanced data, especially for the minority class data. Existing work on improving the quality of classification on imbalanced data include over-sampling, under-sampling, and methods that make modifications to the existing classification systems. This paper discusses the BEV system for classifying imbalanced data. The system is developed based on the ideas from the "Bagging" classification ensemble. The motivation behind the scheme is to maximally use the minority class data without creating synthetic data or making changes to the existing classification systems. Experimental results using real world imbalanced data show the effectiveness of the system.