Lazy Associative Classification is a novel lazy associative classifier that generates classification rules on demand for each test instance, focusing only on relevant features. This approach avoids overfitting and outperforms traditional eager associative classifiers and decision tree classifiers in experiments on 26 datasets. The lazy classifier is able to better handle small disjuncts problems compared to eager approaches and achieves higher accuracy with faster execution times.

1. Lazy Associative Classification Adriano Veloso, Wagner Meira Jr, Mohammed J. Zaki Computer Science Dept, Federal University of Minas Gerais, Brazil Computer Science Dept, Rensselaer Polytechnic Institute, Troy, USA ICDM’06 Reporter: Chieh-Chang Yang Date: 2007.03.19

11. Decision trees and decision rules

13. Entropy-based Associative Classifier

16. Entropy-based Associative Classifier

17. Lazy Associative Classifier

19. Lazy Associative Classifier

27. Comparison Between Decision Trees ,Eager Classifiers, and Lazy Classifiers

28. Comparison Between Decision Trees ,Eager Classifiers, and Lazy Classifiers

29. Comparison Between Rule Induction and Associative Classifiers

30. Overfitting and Underfitting

31. Execution Times