Abstract
Decision trees are the most admired and extensively used classification algorithms in data mining. These are considered accurate, easy to use, and comprehensible classifiers. Like many other classification models, decision tree classifiers ignore exceptions as noise. Exceptions are precious pieces of knowledge that are required to modify our decisions in extraordinarily rare circumstances. Since exceptions pertain to a tiny number of instances, it is a very challenging task to capture exceptions since a learning algorithm’s focus is on discovering knowledge with high generalization power. This paper proposes an enhanced decision tree learning algorithm that accommodates exceptions. The working of the suggested algorithm is demonstrated on a toy example dataset. It is further applied to three datasets obtained from machine learning repository. The results reveal that the Enhanced Decision Tree Algorithm (EDTA) discovers several exceptions across the experimental datasets.
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References
Appavu alias Balamurugan, S., Rajaram, R.: Effective solution for unhandled exception in decision tree induction algorithms. Expert Syst. Appl. 36(10), 12113–12119 (2009)
Bala, R., Ratnoo, S.: Discovering fuzzy censored classification rules (FCCRs): a genetic algorithm approach. Int. J. Artif. Intell. Appl. 3(4), 175–188 (2012)
Bala, R., Ratnoo, S.: A genetic algorithm approach for discovering tuned fuzzy classification rules with intra- and inter-class exceptions. J. Intell. Syst. 25, 263–282 (2016)
Bharadwaj, K.K., Al-Maqaleh, B.M.: Evolutionary approach for automated discovery of censored production rules 1(10), 3230–3235 (2007)
Compton, P., et al.: Ripple down rules: turning knowledge acquisition into knowledge maintenance. Artif. Intell. Med. 4(6), 463–475 (1992)
Carvalho, D.R., Freitas, A.A.: A hybrid decision tree/genetic algorithm method for data mining. Inf. Sci. 163(1), 13–35 (2004)
Dietterich, T.G.: Ensemble methods in machine learning. In: Multiple Classifier Systems, pp. 1–15. Springer, Berlin Heidelberg (2000)
Gehrke, J., et al.: Rain forest—a framework for fast decision tree construction of large datasets. Data Min. Knowl. Disc. 4(2–3), 127–162 (2000)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9 (2006)
Gill, A.A., et al.: Improving decision tree performance through induction- and cluster-based stratified sampling. In: Intelligent Data Engineering and Automated Learning, pp. 339–344. Springer, Berlin, Heidelberg (2004) (LNCS, 7177)
Han, J., et al.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier (2011)
Kotsiantis, S.B.: Decision trees: a recent overview. Artif. Intell. Rev. 39(4), 261–283 (2013)
Lim, T.-S., et al.: A Comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)
Mehta, M., et al.: SLIQ: a fast scalable classifier for data mining. In: Advances in Database Technology, pp. 18–32. Springer, Berlin, Heidelberg (1996) (LNCS, 1057)
Saroj, S., Bharadwaj, K.K.: A parallel genetic algorithm approach for automated discovery of censored production rules. In: Proceedings of the 25th International Multi-Conference: Artificial Intelligence and Applications, pp. 435–441. ACTA Press, Anaheim, CA, USA (2007)
Shafer, J.. et al.: SPRINT: a scalable parallel classifier for data mining. In: Proceedings of the 22nd International Conference on Very Large Databases, pp. 544–555. Morgan Kaufmann (1996)
Su, J., Zhang, H.: A fast decision tree learning algorithm. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 500–505. AAAI Press, Boston, MA (2006)
Subramanian, A.A.B., et al.: Improving decision tree performance by exception handling. Int. J. Autom. Comput. 7(3), 372–380 (2010)
Suzuki, E.: Discovering interesting exception rules with rule pair. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop on Advances in Inductive Rule Learning, pp. 163–178. (2004)
Suzuki, E., Zytkow, J.M.: Unified algorithm for undirected discovery of exception rules. In: Principles of Data Mining and Knowledge Discovery, pp. 169–180. Springer, Berlin, Heidelberg (2000) (LNCS, 1910)
Vashishtha, J., et al.: An evolutionary approach to discover intra– and inter-class exceptions in databases. Int. J. Intell. Syst. Technol. Appl. 12(3–4), 283–300 (2013)
Yıldız, O.T., Dikmen, O.: Parallel univariate decision trees. Pattern Recogn. Lett. 28(7), 825–832 (2007)
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Kumar, S., Ratnoo, S., Bala, R. (2020). Enhanced Decision Tree Algorithm for Discovery of Exceptions. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-15-1081-6_52
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DOI: https://doi.org/10.1007/978-981-15-1081-6_52
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