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Extracting qualitative relations from categorical data. (English) Zbl 1386.68146
Summary: Qualitative modeling is traditionally concerned with the abstraction of numerical data. In numerical domains, partial derivatives describe the relation between the independent and dependent variable; qualitatively, they tell us the trend of the dependent variable. In this paper, we address the problem of extracting qualitative relations in categorical domains. We generalize the notion of partial derivative by defining the probabilistic discrete qualitative partial derivative (PDQ PD). PDQ PD is a qualitative relation between the target class \(c\) and the discrete attribute; the derivative corresponds to ordering the attribute’s values, \(a_i\), by \(P(c|a_i)\) in a local neighborhood of the reference point, respecting the ceteris paribus principle. We present an algorithm for computation of PDQ PD from labeled attribute-based training data. Machine learning algorithms can then be used to induce models that explain the influence of the attribute’s values on the target class in different subspaces of the attribute space.
68T05 Learning and adaptive systems in artificial intelligence
CP-nets; Orange
Full Text: DOI
[1] Borštnar, S.; Sadikov, A.; Možina, B.; Čufer, T., High levels of upa and PAI-1 predict a good response to anthracyclines, Breast Cancer Res. Treat., 121, 615-624, (2010)
[2] Samuelson, P. A., Foundations of economic analysis, (1983), Harvard University Press
[3] Kuipers, B., Using qualitative reasoning, IEEE Expert, 12, 3, 94-97, (1997)
[4] Žabkar, J.; Možina, M.; Bratko, I.; Demšar, J., Learning qualitative models from numerical data, Artif. Intell., 175, 9-10, 1604-1619, (2011) · Zbl 1300.68048
[5] Bratko, I.; Šuc, D., Learning qualitative models, AI Mag., 24, 4, 107-119, (2003)
[6] Cestnik, B., Estimating probabilities: a crucial task in machine learning, (ECAI, (1990)), 147-149
[7] Sokal, R. R.; Michener, C. D., A statistical method for evaluating systematic relationships, Univ. Kans. Sci. Bull., 28, 1409-1438, (1958)
[8] Demšar, J.; Curk, T.; Erjavec, A.; Gorup, Črt; Hočevar, T.; Milutinovič, M.; Možina, M.; Polajnar, M.; Toplak, M.; Starič, A.; Štajdohar, M.; Umek, L.; Žagar, L.; Žbontar, J.; Žitnik, M.; Zupan, B., Orange: data mining toolbox in python, J. Mach. Learn. Res., 14, 2349-2353, (2013) · Zbl 1317.68151
[9] Alciatore, D. G., The illustrated principles of pool and billiards, (2004), Sterling
[10] Papavasiliou, D., Billiards manual, (2009), Tech. rep.
[11] Yoshikawa, T. T., Epidemiology and unique aspects of aging and infectious diseases, Clin. Infect. Dis., 30, 6, 931-933, (2000)
[12] High, K. P., Why should the infectious diseases community focus on aging and care of the older adult?, Clin. Infect. Dis., 37, 2, 196-200, (2003)
[13] Curns, A. T.; Holman, R. C.; Sejvar, J. J.; Owings, M. F.; Schonberger, L. B., Infectious disease hospitalizations among older adults in the united states from 1990 through 2002, Arch. Intern. Med., 165, 21, 2514-2520, (2005)
[14] Mouton, C. P.; Bazaldua, O. V.; Pierce, B.; Espino, D. V., Common infections in older adults, Am. Fam. Phys., 63, 2, 257-269, (2001)
[15] de Kleer, J.; Brown, J. S., A qualitative physics based on confluences, Artif. Intell., 24, 7-83, (1984)
[16] Kuipers, B., Qualitative simulation, Artif. Intell., 29, 289-338, (1986) · Zbl 0624.68098
[17] Kuipers, B., Qualitative reasoning: modeling and simulation with incomplete knowledge, (1994), MIT Press Massachusetts
[18] Forbus, K., Qualitative reasoning, (1997), CRC Press
[19] Forbus, K., Qualitative process theory, Artif. Intell., 24, 85-168, (1984)
[20] Klenk, M.; Forbus, K., Analogical model formulation for transfer learning in AP physics, Artif. Intell., 173, 18, 1615-1638, (2009)
[21] Kononenko, I., Semi-naive Bayesian classifier, (EWSL-91: Proceedings of the European Working Session on Learning on Machine Learning, (1991), Springer-Verlag New York, Inc. New York, NY, USA), 206-219
[22] Langley, P.; Sage, S., Induction of selective Bayesian classifiers, (Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, (1994), Morgan Kaufmann), 399-406
[23] Kohavi, R., Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid, (Proceedings of the Second International Conference on Knoledge Discovery and Data Mining, (1996))
[24] Friedman, N.; Goldszmidt, M., Building classifiers using Bayesian networks, (Proceedings of the Thirteenth National Conference on Artificial Intelligence, (1996), AAAI Press), 1277-1284
[25] Keogh, E. J.; Pazzani, M. J., Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches, (Proceedings of the International Workshop on Artificial Intelligence and Statistics, (1999)), 225-230
[26] Meretakis, D.; Wuthrich, B., Extending naive Bayes classifiers using long itemsets, (KDD ’99: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1999), ACM New York, NY, USA), 165-174
[27] Frank, E.; Hall, M.; Pfahringer, B., Locally weighted naive Bayes, (Proceedings of the Conference on Uncertainty in Artificial Intelligence, UAI 2003, (2003))
[28] Zheng, Z.; Webb, G. I.; Ting, K. M., Lazy Bayesian rules: a lazy semi-naive Bayesian learning technique competitive to boosting decision trees, (Proc. 16th International Conf. on Machine Learning, (1999), Morgan Kaufmann San Francisco, CA), 493-502
[29] Fürnkranz, J.; Hüllermeier, E., Preference learning, (2010), Springer-Verlag · Zbl 1214.68285
[30] Aiolli, F.; Sperduti, A., A preference optimization based framework for supervised learning problems, (Fürnkranz, J.; Hüllermeier, E., Preference Learning, (2010), Springer-Verlag), 19-42 · Zbl 1214.68270
[31] Rossi, F.; Venable, K. B.; Walsh, T., A short introduction to preferences: between artificial intelligence and social choice, (2011), Morgan and Claypool Publishers
[32] Boutilier, C.; Brafman, R. I.; Hoos, H. H.; Poole, D., CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements, J. Artif. Intell. Res., 21, 2004, (2003)
[33] Chu, W.; Ghahramani, Z., Preference learning with Gaussian processes, (Proceedings of the 22nd International Conference on Machine Learning, ICML ’05, (2005), ACM New York, NY, USA), 137-144
[34] Brochu, E.; de Freitas, N.; Ghosh, A., Active preference learning with discrete choice data, (Advances in Neural Information Processing Systems, (2007))
[35] Cheng, W.; Huhn, J. C.; Hüllermeier, E., Decision tree and instance-based learning for label ranking, (Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, (2009)), 161-168
[36] Chevaleyre, Y.; Koriche, F.; Lang, J.; Mengin, J.; Zanuttini, B., Learning ordinal preferences on multiattribute domains: the case of CP-nets, (Fürnkranz, J.; Hüllermeier, E., Preference Learning, (2009), Springer-Verlag), 273-296 · Zbl 1214.68277
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