Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
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
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Emerging Patterns and Classification
ASIAN '00 Proceedings of the 6th Asian Computing Science Conference on Advances in Computing Science
Combining the Strength of Pattern Frequency and Distance for Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Efficient Mining of Niches and Set Routines
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
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
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
A simple statistical model and association rule filtering for classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
DRC-BK: mining classification rules by using Boolean kernels
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
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Emerging patterns (EPs), namely itemsets whose supports change significantly from one class to another, capture discriminating features that sharply contrast instances between the classes. Recently, EP-based classifiers have been proposed, which first mine as many EPs as possible (called eager-learning) from the training data and then aggregate the discriminating power of the mined EPs for classifying new instances. We propose here a new, instance-based classifier using EPs, called DeEPs, to achieve much better accuracy and efficiency than the previously proposed EP-based classifiers. High accuracy is achieved because the instance-based approach enables DeEPs to pinpoint all EPs relevant to a test instance, some of which are missed by the eager-learning approaches. High efficiency is obtained using a series of data reduction and concise data-representation techniques. Experiments show that DeEPs' decision time is linearly scalable over the number of training instances and nearly linearly over the number of attributes. Experiments on 40 datasets also show that DeEPs is superior to other classifiers on accuracy.