zbMATH — the first resource for mathematics

Uncertain decision tree for bank marketing classification. (English) Zbl 1430.68295
Summary: This study proposes a novel decision tree for uncertain data, called the uncertain decision tree (UDT), based on the uncertain genetic clustering algorithm (UGCA). UDT extends the decision tree to handle data with uncertain information, in which the uncertainty must be considered to obtain high quality results. In UDT, UGCA automatically searches for the proper number of branches of each node, based on the classification error rate and the classification time of UDT. Restated, UGCA reduces both the classification error rate and computing time and, then, optimizes the proposed UDT. Before the UDT is designed using UGCA, an uncertain merging algorithm (UMA) is also developed to reduce the uncertain data set, thereby allowing UGCA to process a large data set efficiently. Importantly, experimental results demonstrate that the proposed UDT outperforms traditional uncertain decision trees.
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T37 Reasoning under uncertainty in the context of artificial intelligence
90B60 Marketing, advertising
C4.5; UCI-ml
Full Text: DOI
[1] Quinlann, J. R., Induction of decision tree, Mach. Learn., 1, 81-106 (1986)
[2] Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J., Vlassification and Regression Trees (1984), Wadsworth: Wadsworth Belmont, CA
[3] A. Dobra, M. Schlosser, Non-linear decision trees-NDT, in: Proc. 13th Int. Conf. Machine Learning (ICML’96), 1996, pp. 3-6.
[4] Murthy, S. K.; Kasif, S.; Salzberg, S., A system for induction of oblique decision trees, J. Artif. Intell. Res., 2, 1-32 (1994) · Zbl 0900.68335
[5] R. Weber, Fuzzy ID3; A class of methods for automatic knowledge acquisition, in: Proc. 2nd Int. Conf. Fuzzy Logic Neural Networks, 1992, pp. 265-268.
[6] Gelfand, S. B.; Ravishankar, C. S.; Delp, E. J., An iterative growing and pruning algorithm for classification tree design, IEEE Trans. Pattern Anal. Mach. Intell., 13, 3, 163-174 (1991)
[7] Yildiz, O. T.; Alpaydin, E., Omnivariate decision trees, IEEE Trans. Neural Netw., 12, 6, 1539-1546 (2001)
[8] Zhao, H.; Ram, S., Constrained cascade generalization of decision trees, IEEE Trans. Knowl. Data Eng., 16, 6, 727-739 (2004)
[9] Gonzalo, M. M.; Alberto, S., Using all data to generate decision tree ensembles, IEEE Trans. Syst. Man Cybern. C, 34, 4, 393-397 (2004)
[10] Wang, X.; Chen, B.; Qian, G.; Ye, F., On the optimization of fuzzy decision trees, Fuzzy Sets and Systems, 11, 2, 117-125 (2000)
[11] Quinlan, J. R., Introduction of decision trees, Mach. Learn., 1, 1, 81-106 (1986)
[12] Wang, X. Z.; Yeung, D. S.; Tsang, E. C.C., A comparative study on heuristic algorithms for generating fuzzy decision trees, IEEE Trans. Syst. Man Cybern. B, 31, 2, 215-226 (2001)
[13] Quinlan, J. R., C4.5: Programs for Machine Learning (1993), Morgan Kaufmann
[14] Bezdek, J. C., Pattern Recognition with Fuzzy Objective Functions (1981), PlenUDMA: PlenUDMA New York · Zbl 0503.68069
[15] Pedrycz, W.; Sosnowski, A., Designing decision trees with the use of fuzzy granulation, IEEE Trans. Syst. Man Cybern. A, 30, 151-159 (2000)
[16] Witold, P.; Zenon, A. S., C-fuzzy decision trees, IEEE Trans. Syst. Man Cybern. C, 35, 498-511 (2005)
[17] Hu, Y.; Wu, D.; Nucci, A.o., Fuzzy-clustering-based decision tree approach for large population speaker identification, IEEE Trans. Audio Speech Lang. Process., 21, 762-774 (2013)
[18] Daida, J.; Polito, J., What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming, Genet. Program. Evol. Mach., 2, 2, 165-191 (2001) · Zbl 1035.68587
[19] Daida, J.; Li, H.; Tang, R.; Hilss, A., What makes a problem GP-hard? Validating a hypothesis of structural causes, (Proc. Genetic Algorithms Evol. Comput. Conf.. Proc. Genetic Algorithms Evol. Comput. Conf., Lecture Notes Computer Science (2003)), 1665-1677 · Zbl 1038.68670
[20] Yi, L.; Wanli, K., A new genetic programming algorithm for building decision tree, Procedia Eng., 15, 3, 3658-3662 (2011)
[21] Shukla, S. K.; Tiwari, M. K., GA guided cluster based fuzzy decision tree for reactive ion etching modeling: A data mining approach, IEEE Trans. Semicond. Manuf., 25, 1, 45-56 (2012)
[22] M. Chau, R. Cheng, B. Kao, J. Ng, Uncertain data mining: An example in clustering location data, in: Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD), 2006, pp. 199-204.
[23] W.K. Ngai, B. Kao, C.K. Chui, R. Cheng, M. Chau, K.Y. Yip, Efficient clustering of uncertain data, in: Proc. Int’l Conf. Data Mining (ICDM), 2006, pp. 436-445.
[24] S.D. Lee, B. Kao, R. Cheng, Reducing UK-Means to KMeans, in: Proc. First Workshop Data Mining of Uncertain Data (DUNE), in conjunction with the Seventh IEEE Int’l Conf. Data Mining (ICDM), 2007.
[25] Soundararajan, K.; Sureshkumar, S., Decision tree approach for classifying uncertain data, World Eng. Appl. Sci. J., 7, 2, 74-84 (2016)
[26] Myriam Tami, M. Clausel, E. Devijver, E. Gaussier, J.-M. Aubert, et al. Decision tree for uncertainty measures. 2018. JDS 2018 : 50èmes Journées de statistique, Paris-Saclay.
[27] Genuer, R.; Poggi, J.-M., Arbres CART et Forêts aléatoires, importance et sélection de variables (2016), arXiv preprint 1610.08203
[28] Louppe, G., Understanding Random Forests: From Theory to Practice (2014), Thèse soutenue à l’université de Liège
[29] H.-P. Kriegel, M. Pfeifle, Density-based clustering of uncertain data, in: Proc. Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2005, pp. 672-677.
[30] C.C. Aggarwal, On density based transforms for uncertain data mining, in: Proc. Int’l Conf. Data Eng. (ICDE), 2007, pp. 866-875.
[31] Quinlan, J. R., Learning logical definitions from relations, Mach. Learn., 5, 2, 239-266 (1990)
[32] Merz, C.; Murphy, P., UCI Repository of Machine Learning Databases (2006), Dept. of CIS, Univ. of California: Dept. of CIS, Univ. of California Irvine
[33] Moro, S.; Cortez, P.; Rita, P., A data-driven approach to predict the success of bank telemarketing, Decis. Support Syst., 62, 22-31 (2014), Elsevier
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.