Decision-Tree-Based Knowledge Discovery: Single- vs. Multi-Decision-Tree Induction
Published Online:1 Feb 2008https://doi.org/10.1287/ijoc.1060.0215
References
- Data Mining Techniques: For Marketing, Sales, and Customer Support (1997) (John Wiley & Sons, New York) Google Scholar
- Mastering Data Mining: The Art and Science of Customer Relationship Management (2000) (John Wiley & Sons, New York) Google Scholar
- A further comparison of splitting rules for decision-tree induction. Machine Learn. (1992) 8:75–85Crossref, Google Scholar
- ASSISTANT 86: A knowledge-elicitation tool for sophisticated users. Proc. 2nd Eur. Working Session Learn. (1987) (Sigma Press, Bled, Yugoslavia) 31–45Google Scholar
- Improved decision trees: A generalized version of ID3. Proc. 5th Internat. Conf. Machine Learn. (1988) (Morgan Kaufmann, San Mateo, CA) 100–106Crossref, Google Scholar
- 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
- 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
- Solving multiclass learning problems via error-correcting output codes. J. Artificial Intelligence Res. (1995) 2:263–286Crossref, Google Scholar
- , 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., Shapiro G. P., Matheus C. J. Knowledge discovery in databases: An overview. Knowledge Discovery in Databases (1991) (MIT Press, Cambridge, MA) 1–27Google Scholar
- Experiments in Induction (1966) (Academic Press, New York) Google Scholar
- , 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
- An empirical comparison of selection measures for decision-tree induction. Machine Learn. (1989) 3:19–342Crossref, Google Scholar
- 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
- Induction of decision trees. Machine Learn. (1986) 1:81–106Crossref, Google Scholar
- C4.5: Programs for Machine Learning (1993) (Morgan Kaufmann, San Mateo, CA) Google Scholar
- Improving the design of similarity-based rule-learning systems. Internat. J. Expert Systems (1989) 2:97–133Google Scholar
- 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
- Engineering patient image retrieval knowledge. Heuristics: J. Knowledge Engrg. Tech. (1994) 3:46–61Google Scholar
- Automated learning of patient image retrieval knowledge: Neural networks versus inductive decision trees. Decision Support System (2000) 30:105–124Crossref, Google Scholar
- Dynamics of modeling in data mining: Interpretative approach to bankruptcy prediction. J. Management Inform. Systems (1999) 16:63–85Crossref, Google Scholar
- Managerial applications of neural networks: The case of bank failure predictions. Management Sci. (1992) 38:926–947Link, Google Scholar
- Incremental induction of decision trees. Machine Learn (1989) 4:161–186Crossref, Google Scholar
- , 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

