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
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
A method of association rule analysis for incomplete database using genetic network programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Association rule mining with chi-squared test using alternate genetic network programming
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evolutionary method for associative local distribution rule mining
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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An associative classification method for incomplete database is proposed based on an evolutionary rule extraction method. The method can extract class association rules directly from the database including missing values and build an associative classifier. Instances including missing values are classified by the classifier. In addition, an evolving associative classifier is proposed. The proposed method evolves the classifier using the labeled instances by itself as acquired information. The performance of the classification was evaluated using artificial incomplete data set. The results showed that the proposed evolving associative classifier has a potential to expand the target data for classification through its evolutionary process and gather useful information itself.