Decision-Tree-Based Knowledge Discovery: Single- vs. Multi-Decision-Tree Induction

Published Online:https://doi.org/10.1287/ijoc.1060.0215

References

  • Berry M. J. A., Linoff G. S.Data Mining Techniques: For Marketing, Sales, and Customer Support (1997) (John Wiley & Sons, New York) Google Scholar
  • Berry M. J. A., Linoff G. S.Mastering Data Mining: The Art and Science of Customer Relationship Management (2000) (John Wiley & Sons, New York) Google Scholar
  • Buntine W., Niblett T. A further comparison of splitting rules for decision-tree induction. Machine Learn. (1992) 8:75–85CrossrefGoogle Scholar
  • Cestnik B., Kononenko I., Bratko I. ASSISTANT 86: A knowledge-elicitation tool for sophisticated users. Proc. 2nd Eur. Working Session Learn. (1987) (Sigma Press, Bled, Yugoslavia) 31–45Google Scholar
  • Cheng J., Fayyad U., Irani K., Qian Z. Improved decision trees: A generalized version of ID3. Proc. 5th Internat. Conf. Machine Learn. (1988) (Morgan Kaufmann, San Mateo, CA) 100–106CrossrefGoogle Scholar
  • Cherkauer K. J., Shavlik J. W. Growing simpler decision trees to facilitate knowledge discovery. Proc. 2nd Internat. Conf. Knowledge Discovery and Data Mining (1996) (AAAI Press, Portland, OR) 315–318Google Scholar
  • Ciesielski V., Palstra G. Using a hybrid neural/expert system for data base mining in marketing survey data. Proc. 2nd Internat. Conf. Knowledge Discovery and Data Mining (1996) (AAAI Press, Portland, OR) 38–43Google Scholar
  • Dietterich T. G., Bakiri G. Solving multiclass learning problems via error-correcting output codes. J. Artificial Intelligence Res. (1995) 2:263–286CrossrefGoogle Scholar
  • Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. From data mining to knowledge discovery: An overview. Advances in Knowledge Discovery Data Mining (1996) (MIT Press, Cambridge, MA) 1–34Google Scholar
  • Frawley W. J., Piatetsky-Shapiro G., Matheus C. J., Frawley W. J., Shapiro G. P., Matheus C. J. Knowledge discovery in databases: An overview. Knowledge Discovery in Databases (1991) (MIT Press, Cambridge, MA) 1–27Google Scholar
  • Hunt E., Martin J., Stone P.Experiments in Induction (1966) (Academic Press, New York) Google Scholar
  • Long J. M., Irani E. A., Slagle J. R., Frawley W. J., Shapiro G. P., Matheus C. J. Automating the discovery of causal relationships in a medical records database. Knowledge Discovery in Databases (1991) (MIT Press, Cambridge, MA) 465–476Google Scholar
  • Mingers J. An empirical comparison of selection measures for decision-tree induction. Machine Learn. (1989) 3:19–342CrossrefGoogle Scholar
  • Murthy S., Salzberg S. Decision tree induction: How effective is the greedy heuristics. Proc. 1st Internat. Conf. Knowledge Discovery and Data Mining (1995) (AAAI Press, Montreal) 222–227Google Scholar
  • Quinlan J. R. Induction of decision trees. Machine Learn. (1986) 1:81–106CrossrefGoogle Scholar
  • Quinlan J. R.C4.5: Programs for Machine Learning (1993) (Morgan Kaufmann, San Mateo, CA) Google Scholar
  • Rendell L., Cho H., Seshu R. Improving the design of similarity-based rule-learning systems. Internat. J. Expert Systems (1989) 2:97–133Google Scholar
  • Sheng O. R. Liu, Chang N. Automated decision rule discovery from domains with joint decision outcomes: A decision tree induction approach. Proc. 3rd Internat. Conf. ISDSS (1995) (Elsevier, Hong Kong) 259–267Google Scholar
  • Sheng O. R. Liu, Wei C., Hu P. Engineering patient image retrieval knowledge. Heuristics: J. Knowledge Engrg. Tech. (1994) 3:46–61Google Scholar
  • Sheng O. R. Liu, Wei C., Hu P., Chang N. Automated learning of patient image retrieval knowledge: Neural networks versus inductive decision trees. Decision Support System (2000) 30:105–124CrossrefGoogle Scholar
  • Sung T. K., Chang N., Lee G. Dynamics of modeling in data mining: Interpretative approach to bankruptcy prediction. J. Management Inform. Systems (1999) 16:63–85CrossrefGoogle Scholar
  • Tam K. Y., Kiang M. Y. Managerial applications of neural networks: The case of bank failure predictions. Management Sci. (1992) 38:926–947LinkGoogle Scholar
  • Utgoff P. E. Incremental induction of decision trees. Machine Learn (1989) 4:161–186CrossrefGoogle Scholar
  • Uthurusamy R., Fayyad U. M., Spangler S., Frawley W. J., Shapiro G. P., Matheus C. J. Learning useful rules from inconclusive data. Knowledge Discovery in Databases (1991) (MIT Press, Cambridge, MA) 141–158Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.