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From global to local and viceversa: uses of associative rule learning for classification in imprecise environments

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Abstract

We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model implements a global-to-local scheme, where the response of a global rule-based classifier is refined by performing a probabilistic analysis of the coverage of its rules. In particular, the coverage of the individual rules is used to learn local probabilistic models, which ultimately refine the predictions from the corresponding rules of the global classifier. The second model implements a dual local-to-global strategy, in which single classification rules are combined within an exponential probabilistic model in order to boost the overall performance as a side effect of mutual influence. Several variants of the basic ideas are studied, and their performances are thoroughly evaluated and compared with state-of-the-art algorithms on standard benchmark datasets.

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References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of international conference on very large data bases, 1994, pp 487–499

  2. Antonie ML, Zaïane OR (2002) Text document categorization by term association. In: Proceedings of IEEE international conference on data mining, 2002, pp 19–26

  3. Antonie ML, Zaïane OR (2004) An associative classifier based on positive and negative rules. In: Proceedings of the ACM SIGMOD workshop on research issues in data mining and knowledge discovery, 2004, pp 64–69

  4. Arunasalam B, Chawla S (2006) CCCS: a top-down association classifier for imbalanced class distribution. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, 2006, pp 517–522

  5. Asuncion A, Newman DJ (2007) UCI, machine learning repository. School of Information and Computer Sciences, University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepositoryhtml

  6. Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Disc 5(3): 213–246

    Article  MATH  Google Scholar 

  7. Berger AL, Della Pietra VJ, Della Pietra SA (1996) A maximum entropy approach to natural language processing. J Artif Intell Res 22(1): 39–71

    Google Scholar 

  8. Cesario E, Folino F, Locane A, Manco G, Ortale R (2008) Boosting text segmentation via progressive classification. Knowl Inf Syst 15(3): 285–320

    Article  Google Scholar 

  9. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1): 321–357

    MATH  Google Scholar 

  10. Chawla NV, Lazarevic A, Hall LO, Bowyer K (2003) SMOTEBoost: improving prediction of minority class in boosting. In: Proceedings of principles of knowledge discovery in databases, 2003, pp 107–119

  11. Cheng H, Yan X, Han J, Hsu CW (2007) Discriminative frequent pattern analysis for effective classification. In: Proceedings of international conference on data engineering, 2007, pp 716–725

  12. Coenen F (2004) LUCS KDD implementations of CBA and CMAR. Department of Computer Science, The University of Liverpool, UK. http://wwwcsclivacuk/~frans/KDD/Software/

  13. Cohen WW (1995) Fast effective rule induction. In: Proceedings of conference on machine learning, 1995, pp 115–123

  14. Cong G, Xu X, Pan F, Tung A, Yang J (2004) FARMER: finding interesting rule groups in microarray datasets. In: Proceedings of ACM SIGMOD international conference on management of data, 2004, pp 123–126

  15. Costa G, Guarascio M, Manco G, Ortale R, Ritacco E (2009) Rule learning with probabilistic smoothing. In: Proceedings of international conference on data warehousing and knowledge discovery, 2009, pp 428–440

  16. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  17. Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of international joint conference on artificial intelligence, 2001, pp 973–978

  18. Ezawa K, Singh M, Norton SW (1996) Learning goal oriented Bayesian networks for telecommunications risk management. In: Proceedings of international conference on machine learning, 1996, pp 139–147

  19. Fan W, Stolfo SJ, Zhang J, Chan PK (1999) AdaCost: misclassification cost-sensitive boosting. In: Proceedings of international conference on machine learning, 1999, pp 97–105

  20. Fawcett RE, Provost F (1997) Adaptive fraud detection. Data Min Knowl Disc 3(1): 291–316

    Article  Google Scholar 

  21. Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. In: Proceedings of international conference on machine learning, 1998, pp 144–151

  22. Hämäläinen W (2010) StatApriori: an efficient algorithm for searching statistically significant association rules. Knowl Inf Syst 23(3): 373–399

    Article  Google Scholar 

  23. Han J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD international conference on management of data, 2000, pp 1–12

  24. Holte RC, Acker L, Porter B (1989) Concept learning and the problem of small disjuncts. In: Proceedings of international conference on artificial intelligence, 1989, pp 813–818

  25. Japkowicz N (2000) The class imbalance Problem: Significance and Strategies. In: Proceedings of international conference on artificial intelligence, 2000, pp 111–117

  26. Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5): 429–449

    MATH  Google Scholar 

  27. Joshi MV, Agarwal RC, Kumar V (2002) Predicting rare classes: can boosting make any weak learner strong? In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, 2002, pp 297–306

  28. Joshi MV, Kumar V, Agarwal RC (2001) Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: Proceedings of IEEE international conference on data mining, 2001, pp 257–264

  29. Kubat M, Holte RC, Matwin S, Kohavi R, Provost F (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(2): 192–215

    Article  Google Scholar 

  30. Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of international conference on machine learning, 1997, pp 179–186

  31. Li W, Han J, Pei J (2001) CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of IEEE international conference on data mining, 2001, pp 369–376

  32. Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, 1998, pp 80–86

  33. Liu B, Ma Y, Wong CK (2000) Improving an association rule based classifier. In: Proceedings of principles of data mining and knowledge discovery, 2000, pp 504–509

  34. McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York

    Book  MATH  Google Scholar 

  35. Mitchell TM (1997) Machine learning. McGraw-Hill, New York

    MATH  Google Scholar 

  36. Pazzani M, Merz C, Murphy P, Hume T, Brunk C (1994) Reducing misclassification costs. In: Proceedings of international conference on machine learning, 1994, pp 217–225

  37. Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explor Newsl. Special issue on learning from imbalanced datasets:50–59

  38. Provost F, Fawcett T (2001) Robust classification for imprecise environments. Mach Learn 42(3): 203–231

    Article  MATH  Google Scholar 

  39. Quinlan JR, Cameron-Jones RM (1993) FOIL: a midterm report. In: Proceedings of European conference on machine learning, 1993, pp 3–20

  40. Riddle P, Segal R, Etzioni O (1994) Representation design and brute-force induction in a Boeing manufacturing domain. Appl Artif Intell 8(1): 125–147

    Article  Google Scholar 

  41. Tang J, Chen Z, Fu A, Cheung D (2007) Capabilities of outlier detection schemes in large datasets, framework and methodologies. Knowl Inf Sys 11(1): 45–84

    Article  Google Scholar 

  42. Tatti N (2008) Maximum entropy based significance of itemsets. Knowl Inf Sys 17(1): 57–77

    Article  Google Scholar 

  43. Thabtah F (2007) A review of associative classification mining. J Knowl Eng Rev 22(1): 37–65

    Article  Google Scholar 

  44. Ting KM (2000) A comparative study of cost-sensitive boosting algorithms. In: Proceedings of international conference on machine learning, 2000, pp 983–990

  45. Wang J, Karypis G (2005) HARMONY: efficiently mining the best rules for classification. In: Proceedings of SIAM international conference on data mining, 2005, pp 205–216

  46. Webb G, Boughton J, Wang Z (2005) Not so naive Bayes: aggregating one-dependence estimators. Mach Learn 58(1): 5–24

    Article  MATH  Google Scholar 

  47. Weiss GM (2000) Learning with rare cases and small disjuncts. In: Proceedings of international conference on machine learning, 2000, pp 558–565

  48. Weiss GM (2004) Mining with rarity: a unifying framework. ACM SIGKDD Explor Newsl 6(1): 7–19

    Article  Google Scholar 

  49. Weiss GM, Hirsh H (2000) A quantitative study of small disjuncts. In: Proceedings of national conference on artificial intelligence, 2000, pp 665–670

  50. Weiss GM, Provost F (2003) Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res 19: 315–354

    MATH  Google Scholar 

  51. Xin X, Han J (2003) CPAR: classification based on predictive association rules. In: Proceedings of SIAM international conference on data mining, 2003, pp 331–335

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Authors and Affiliations

  1. ICAR-CNR, Via Bucci 41c, 87036, Rende, CS, Italy

    Gianni Costa, Giuseppe Manco, Riccardo Ortale & Ettore Ritacco

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  1. Gianni Costa
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  2. Giuseppe Manco
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  3. Riccardo Ortale
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  4. Ettore Ritacco
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Correspondence to Riccardo Ortale.

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Costa, G., Manco, G., Ortale, R. et al. From global to local and viceversa: uses of associative rule learning for classification in imprecise environments. Knowl Inf Syst 33, 137–169 (2012). https://doi.org/10.1007/s10115-011-0458-5

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  • DOI: https://doi.org/10.1007/s10115-011-0458-5

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