Communications of the ACM - Special issue on parallelism
The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Information-Based Classification by Aggregating Emerging Patterns
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
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CAEP, namely Classification by Aggregating Emerging Patterns, builds classifiers from Emerging Patterns (EPs). EPs mined from the training data of a class are distinguishing features of the class. To classify a test instance t, the scores by aggregating EPs in t measures the weight we put on each class; direct comparison of scores decides t's class. However the skewed distribution of EPs among classes and intricate relationship between EPs sometimes make the decision by directly comparing scores unreliable. In this paper, we propose to build Score Behaviour Knowledge Space (SBKS) to record the behaviour of training data on scores; classification decision is drawn from SBKS from a statistical point of view. Extensive experiments on real-world datasets show that SBKS frequently improves CAEP classifiers, especially on datasets where they have relatively poor performance. The improved CAEP classifiers outperform the start-of-the-art decision tree classifier C5.0.