Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Estimating campaign benefits and modeling lift
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scoring the Data Using Association Rules
Applied Intelligence
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
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Efficient implementation of associative classifiers for document classification
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Adapting the CBA algorithm by means of intensity of implication
Information Sciences: an International Journal
Hi-index | 12.05 |
Having received considerable interest in recent years, associative classification has focused on developing a class classifier, with lesser attention paid to the probability classifier used in direct marketing. While contributing to this integrated framework, this work attempts to increase the prediction accuracy of associative classification on class imbalance by adapting the scoring based on associations (SBA) algorithm. The SBA algorithm is modified by coupling it with the pruning strategy of association rules in the probabilistic classification based on associations (PCBA) algorithm, which is adjusted from the CBA for use in the structure of the probability classifier. PCBA is adjusted from CBA by increasing the confidence through under-sampling, setting different minimum supports (minsups) and minimum confidences (minconfs) for rules of different classes based on each distribution, and removing the pruning rules of the lowest error rate. Experimental results based on benchmark datasets and real-life application datasets indicate that the proposed method performs better than C5.0 and the original SBA do, and the number of rules required for scoring is significantly reduced.