C4.5: programs for machine learning
C4.5: programs for machine learning
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
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
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Efficient Mining of Niches and Set Routines
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Efficient String Mining under Constraints Via the Deferred Frequency Index
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Condensed representation of EPs and patterns quantified by frequency-based measures
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Classification aims to discover a model from training data that can be used to predict the class of test instances. In this paper, we propose the use of jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-Classifier. Each JEP can capture some crucial difference between a pair of datasets. Then, aggregating all JEPs of large supports can produce more potent classification power. Procedurally, the JEP-Classifier learns the pair-wise features (sets of JEPs) contained in the training data, and uses the collective impacts contributed by the most expressive pair-wise features to determine the class labels of the test data. Using only the most expressive JEPs in the JEP-Classifier strengthens its resistance to noise in the training data, and reduces its complexity (as there are usually a very large number of JEPs). We use two algorithms for constructing the JEP-Classifier which are both scalable and efficient. These algorithms make use of the border representation to efficiently store and manipulate JEPs. We also present experimental results which show that the JEP-Classifier achieves much higher testing accuracies than the association-based classifier of [8], which was reported to outperform C4.5 in general.