Abstract
CAEP, namely Classification by Aggregating Emerging Patterns, builds classifiers from Emerging Patterns (EPs). EPs mined from the training data of a class are distinguishing features of the class. To classify a test instance t, the scores by aggregating EPs in t measures the weight we put on each class; direct comparison of scores decides t’s class. However the skewed distribution of EPs among classes and intricate relationship between EPs sometimes make the decision by directly comparing scores unreliable. In this paper, we propose to build Score Behaviour Knowledge Space (SBKS) to record the behaviour of training data on scores; classification decision is drawn from SBKS from a statistical point of view. Extensive experiments on real-world datasets show that SBKS frequently improves CAEP classifiers, especially on datasets where they have relatively poor performance. The improved CAEP classifiers outperform the start-of-the-art decision tree classifier C5.0.
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References
G Dong and J Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. 1999ACM SIGKDD Conf., pages 15–18, USA, Aug. 1999.
G Dong, X Zhang, L Wong, and J Li. CAEP: Classification by aggregating emerging patterns. In Proc. DS’99, LNAI 1721, Tokyo, Japan, Dec. 1999.
YS Huang and CY Suen. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Recognition and Machine Intelligence, 17(1):90–94, Jan. 1995.
JR Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
Rulequest Research Pty Ltd. See5/C5.0. http://www.rulequest.com, 1999.
RE Schapire. The strength of weak learnability. Machine Learning, 5(2):197–227, 1990.
C Stanfill and D Waltz. Toward memory-based reasoning. Communications of ACM, 29:1213–1228, 1986.
X Zhang, G Dong, and K Ramamohanarao. Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In Proc 2000 ACM SIGKDD Conf., pages 310–314, Boston, USA, Aug. 2000.
X Zhang, G Dong, and K Ramamohanarao. Information-based classification by aggregating emerging patterns. In Proc IDEAL’2000, LNCS 1983, HK, 2000.
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© 2001 Springer-Verlag Berlin Heidelberg
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Zhang, X., Dong, G., Ramamohanarao, K. (2001). Building Behaviour Knowledge Space to Make Classification Decision. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_51
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DOI: https://doi.org/10.1007/3-540-45357-1_51
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