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Rule-based Classification

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Encyclopedia of Database Systems

Definition

The term rule-based classification can be used to refer to any classification scheme that make use of IF-THEN rules for class prediction. Rule-based classification schemes typically consist of the following components:

  • Rule Induction Algorithm This refers to the process of extracting relevant IF-THEN rules from the data which can be done directly using sequential covering algorithms [1,2,5–7,9,12,14–16] or indirectly from other data mining methods like decision tree building [11,13] or association rule mining [3,4,8,10].

  • Rule Ranking Measures This refers to some values that are used to measure the usefulness of a rule in providing accurate prediction. Rule ranking measures are often used in the rule induction algorithm to prune off unnecessary rules and improve efficiency. They are also used in the class prediction algorithm to give a ranking to the rules which will be then be utilized to predict the class of new cases.

  • Class Prediction AlgorithmGiven a new record with...

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Recommended Reading

  1. Clark P. and Niblett T. The CN2 induction algorithm. Mach. Learn., 3(4):261–283, 1989.

    Google Scholar 

  2. Cohen W. Fast effective rule induction. In Proc. 12th Int. Conf. on Machine Learning, 1995, pp. 115–123.

    Google Scholar 

  3. Cong G., Tan K., Tung A., and Xu X. Mining top-K covering rule groups for gene expression data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2005, pp. 670–681.

    Google Scholar 

  4. Cong G. Tung A.K.H., Xu X., Pan F., and Yang J. FARMER: finding interesting rule groups in microarray datasets. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2004, pp. 143–154.

    Google Scholar 

  5. Domingos P. The RISE system: conquering without separating. Tools with Artificial Intelligence, 1994. In Proc. 6th IEEE Int. Conf. on Tools with Artificial Intelligence, 1994, pp. 704–707.

    Google Scholar 

  6. Furnkranz J. and Widmer G. Incremental reduced error pruning. In Proc. 11th Int. Conf. on Machine Learning, 1994, pp. 70–77.

    Google Scholar 

  7. Hong J., Mozetic I., and Michalski R. AQ15: Incremental Learning of Attribute-Based Descriptions from Examples: The Method and User’s Guide. Reports of the Intelligent Systems Group, ISG, pp. 86–5.

    Google Scholar 

  8. Liu B., Hsu W., and Ma Y. Integrating Classification and Association Rule Mining. In Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, 1998.

    Google Scholar 

  9. Major J. and Mangano J. Selecting among rules induced from a hurricane database. J. Intell. Inform. Syst., 4(1):39–52, 1995.

    Google Scholar 

  10. Pan F., Cong G., and Tung A.K.H. CARPENTER: Finding closed patterns in long biological datasets. In Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2003.

    Google Scholar 

  11. Quinlan J. Simplifying decision trees. Int. J. Man–Machine Studies, 27(3):221–234, 1987.

    Google Scholar 

  12. Quinlan J. Learning logical definitions from relations. Mach. Learn., 5(3):239–266, 1990.

    Google Scholar 

  13. Quinlan J. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  14. Quinlan J. and Cameron-Jones R. FOIL: A Midterm Report. In Proc. European Conf. on Machine Learning, 1993.

    Google Scholar 

  15. Smyth P. and Goodman R. An information theoretic approach to rule induction from databases. IEEE Trans. Knowl. Data Eng., 4(4):301–316, 1992.

    Google Scholar 

  16. Weiss S. and Indurkhya N. Predictive Data Mining: A Practical Guide. Morgan Kaufmann, Los Altos, CA, 1998.

    MATH  Google Scholar 

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Tung, A.K.H. (2009). Rule-based Classification. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_559

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