C4.5: programs for machine learning
C4.5: programs for machine learning
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A new approach to classification based on association rule mining
Decision Support Systems
Using metarules to organize and group discovered association rules
Data Mining and Knowledge Discovery
A decision support system for detecting products missing from the shelf based on heuristic rules
Decision Support Systems
Open-source machine learning: R meets Weka
Computational Statistics - Proceedings of DSC 2007
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
The Journal of Machine Learning Research
Using data mining techniques to predict hospitalization of hemodialysis patients
Decision Support Systems
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Performance of classification models from a user perspective
Decision Support Systems
Mining actionable behavioral rules
Decision Support Systems
Gene selection with guided regularized random forest
Pattern Recognition
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Associative classifiers have been proposed to achieve an accurate model with each individual rule being interpretable. However, existing associative classifiers often consist of a large number of rules and, thus, can be difficult to interpret. We show that associative classifiers consisting of an ordered rule set can be represented as a tree model. From this view, it is clear that these classifiers are restricted in that at least one child node of a non-leaf node is never split. We propose a new tree model, i.e., condition-based tree (CBT), to relax the restriction. Furthermore, we also propose an algorithm to transform a CBT to an ordered rule set with concise rule conditions. This ordered rule set is referred to as a condition-based classifier (CBC). Thus, the interpretability of an associative classifier is maintained, but more expressive models are possible. The rule transformation algorithm can be also applied to regular binary decision trees to extract an ordered set of rules with simple rule conditions. Feature selection is applied to a binary representation of conditions to simplify/improve the models further. Experimental studies show that CBC has competitive accuracy performance, and has a significantly smaller number of rules (median of 10 rules per data set) than well-known associative classifiers such as CBA (median of 47) and GARC (median of 21). CBC with feature selection has even a smaller number of rules.