Abstract
This paper presents a novel decision-tree induction for a multi-objective data set, i.e. a data set with a multi-dimensional class. Inductive decision-tree learning is one of the frequently-used methods for a single-objective data set, i.e. a data set with a single-dimensional class. However, in a real data analysis, we usually have multiple objectives, and a classifier which explains them simultaneously would be useful and would exhibit higher readability. A conventional decision-tree inducer requires transformation of a multi-dimensional class into a single-dimensional class, but such a transformation can considerably worsen both accuracy and readability. In order to circumvent this problem we propose a bloomy decision tree which deals with a multi-dimensional class without such transformations. A bloomy decision tree has a set of split nodes each of which splits examples according to their attribute values, and a set of flower nodes each of which predicts a class dimension of examples. A flower node appears not only at the fringe of a tree but also inside a tree. Our pruning is executed during tree construction, and evaluates each class dimension based on Cramér’s V. The proposed method has been implemented as D3-B (Decision tree in Bloom), and tested with eleven data sets. The experiments showed that D3-B has higher accuracies in nine data sets than C4.5 and tied with it in the other two data sets. In terms of readability, D3-B has a smaller number of split nodes in all data sets, and thus outperforms C4.5.
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Blake, C., Keogh, E., and Merz, C. J.: UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, Univ. California, Irvine, Dept. Information and Computer Science (1998).
Breiman, L., Friedman, J., Olshen, R., and Stone, C. A.: Classification and Regression Trees, Chapman & Hall, New York (1984).
Caruana, R.: “Multitask Learning”, Machine Learning, Vol. 28, No. 1, pp. 41–75 (1997).
Caruana, R.: “Multitask Learning”, Ph. D. Thesis, CMU-CS-97-203, School of Computer Science, Carnegie Mellon Univ., Pittsburgh, Pa. (1997).
Dougherty, J., Kohavi, R., and Sahami M.: “Supervised and Unsupervised Discretization of Continuous Features”, Proc. Twelfth Int’l Conf. on Machine Learning (ICML), pp. 194–202 (1995).
Forouraghi, B., Schmerr, L. W., and Prabhu, G. M.: “Induction of Multivariate Regression Trees for Design Optimization”, Proc. Twelfth Nat’l Conf. on Artificial Intelligence (AAAI), pp. 607–612 (1994).
Kendall, M. G.: Multivariate Analysis, second edition, Charles Griffin, High Wycombe, England (1980).
Mingers, J.: “An Empirical Comparison of Pruning Methods for Decision-Tree Induction”, Machine Learning, Vol. 4, No. 2, pp. 227–243 (1989).
Murthy, S. K.: “Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey”, Data Mining and Knowledge Discovery, Vol. 2, No. 4, pp. 345–389 (1998).
Quinlan, J. R.: “Induction of Decision Trees”, Machine Learning, Vol. 1, No. 1, pp. 81–106 (1986).
Quinlan, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Calif. (1993).
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Suzuki, E., Gotoh, M., Choki, Y. (2001). Bloomy Decision Tree for Multi-objective Classification. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_36
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DOI: https://doi.org/10.1007/3-540-44794-6_36
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