C4.5: programs for machine learning
C4.5: programs for machine learning
Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rule Learning with Probabilistic Smoothing
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Transactions on rough sets XII
Fuzzy multiple support associative classification approach for prediction
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Classification using multiple and negative target rules
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Mining correlated rules for associative classification
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Existing classification algorithms in machine learning mainly use heuristic search to find a subset of regularities in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. Although the whole set of rules may not be used directly for accurate classification, effective classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against RIPPER, LB and a Naïve-Bayes classifier.