Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Using Emerging Patterns to Construct Weighted Decision Trees
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks
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Support vector machine (SVM) classifiers represent one of the most powerful and promising tools for solving classification problems. In the past decade SVMs have been shown to have excellent performance in the field of data mining. The standard SVM classifier treats all instances equally. However, in many applications we have different levels of confidence in different instances that belong to a particular class. Fuzzy SVMs have been used to recognize the importance of each training instance. Although these schemes are called fuzzy SVMs, they are basically trained by weighted training instances. In this paper we propose a new robust weighting scheme for the class memberships for fuzzy SVM classifier. The weighting scheme is a sophisticated and effective method for weighting the training instances which makes use of highly discriminating patterns called emerging patterns (EPs). Our experiments show that this new weighting method has excellent performance and noise tolerance compared to the weighting scheme previously proposed.