Machine Learning
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Machine Learning
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ICDT '01 Proceedings of the 8th International Conference on Database Theory
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PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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Expert Systems with Applications: An International Journal
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IEEE Transactions on Knowledge and Data Engineering
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Hellinger distance decision trees are robust and skew-insensitive
Data Mining and Knowledge Discovery
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In several pattern classification problems, we encounter training datasets with an imbalanced class distribution and the presence of outliers, which can hinder the performance of classifiers. In this paper, we propose classification schemes based on the pre-processing of data using Novel Pattern Synthesis (NPS), with the aim to improve performance on such datasets. We provide a formal framework for characterizing the class imbalance and outlier elimination. Specifically, we look into the role of NPS in: Outlier elimination and handling class imbalance problem. In NPS, for every pattern its k-nearest neighbours are found and a weighted average of the neighbours is taken to form a synthesized pattern. It is found that the classification accuracy of minority class increases in the presence of synthesized patterns. However, finding nearest neighbours in high-dimensional datasets is challenging. Hence, we make use of Latent Dirichlet Allocation to reduce the dimensionality of the dataset. An extensive experimental evaluation carried out on 25 real-world imbalanced datasets shows that pre-processing of data using NPS is effective and has a greater impact on the classification accuracy over minority class for imbalanced learning. We also observed that NPS outperforms the state-of-the-art methods for imbalanced classification. Experiments on 9 real-world datasets with outliers, demonstrate that NPS approach not only substantially increases the detection performance, but is also relatively scalable in large datasets in comparison to the state-of-the-art outlier detection methods.