CMAR: accurate and efficient classification based on multiple class-association rules. Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only a single high-confidence rule. The authors propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large databases efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted /spl chi//sup 2/ analysis using multiple strong association rules. Our extensive experiments on 26 databases from the UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.

References in zbMATH (referenced in 53 articles )

Showing results 1 to 20 of 53.
Sorted by year (citations)

1 2 3 next

  1. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  2. Geng, Xiaojiao; Liang, Yan; Jiao, Lianmeng: EARC: evidential association rule-based classification (2021)
  3. Shabtay, Lior; Fournier-Viger, Philippe; Yaari, Rami; Dattner, Itai: A guided FP-growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data (2021)
  4. Azmi, Mohamed; Runger, George C.; Berrado, Abdelaziz: Interpretable regularized class association rules algorithm for classification in a categorical data space (2019)
  5. Hämäläinen, Wilhelmiina; Webb, Geoffrey I.: A tutorial on statistically sound pattern discovery (2019)
  6. Angelino, Elaine; Larus-Stone, Nicholas; Alabi, Daniel; Seltzer, Margo; Rudin, Cynthia: Learning certifiably optimal rule lists for categorical data (2018)
  7. Rudin, Cynthia; Ertekin, Şeyda: Learning customized and optimized lists of rules with mathematical programming (2018)
  8. Wang, Tong; Rudin, Cynthia; Doshi-Velez, Finale; Liu, Yimin; Klampfl, Erica; Macneille, Perry: A Bayesian framework for learning rule sets for interpretable classification (2017)
  9. Esmi, Estevão; Sussner, Peter; Sandri, Sandra: Tunable equivalence fuzzy associative memories (2016)
  10. Hajian, Sara; Domingo-Ferrer, Josep; Monreale, Anna; Pedreschi, Dino; Giannotti, Fosca: Discrimination- and privacy-aware patterns (2015)
  11. Letham, Benjamin; Rudin, Cynthia; McCormick, Tyler H.; Madigan, David: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model (2015)
  12. Nandhini, M.; Sivanandam, S. N.: An improved predictive association rule based classifier using gain ratio and T-test for health care data diagnosis (2015) ioport
  13. Nguyen, Dang; Nguyen, Loan T. T.; Vo, Bay; Hong, Tzung-Pei: A novel method for constrained class association rule mining (2015)
  14. Bettebghor, Dimitri; Leroy, François-Henri: Overlapping radial basis function interpolants for spectrally accurate approximation of functions of eigenvalues with application to buckling of composite plates (2014)
  15. Khan, Salabat; Baig, Abdul Rauf; Ali, Armughan; Haider, Bilal; Khan, Farman Ali; Durrani, Mehr Yahya; Ishtiaq, Muhammad: Unordered rule discovery using ant colony optimization (2014)
  16. Shaharanee, I. N. M.; Hadzic, F.: Evaluation and optimization of frequent, closed and maximal association rule based classification (2014)
  17. Zheng, Yifeng; Huang, Zaixiang; He, Tianzhong: Classification based on both attribute value weight and tuple weight under the cloud computing (2013) ioport
  18. Le Bras, Yannick; Lenca, Philippe; Lallich, Stéphane: Optimonotone measures for optimal rule discovery (2012)
  19. Meo, Rosa; Bachar, Dipankar; Ienco, Dino: LODE: a distance-based classifier built on ensembles of positive and negative observations (2012) ioport
  20. Policicchio, Veronica L.; Pietramala, Adriana; Rullo, Pasquale: GAMoN: discovering (M)-of-(N^\neg, \lor) hypotheses for text classification by a lattice-based genetic algorithm (2012)

1 2 3 next