ACM Home Page
Please provide us with feedback. Feedback
The HCV induction algorithm
Full text PdfPdf (890 KB)
Source ACM Annual Computer Science Conference archive
Proceedings of the 1993 ACM conference on Computer science table of contents
Indianapolis, Indiana, United States
Pages: 168 - 175  
Year of Publication: 1993
ISBN:0-89791-558-5
Author
Xindong Wu  Department of Artificial Intelligence, University of Edinburgh, 80 South Bridge, Edinburgh EH1 1HN, UK
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 25,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/170791.170825
What is a DOI?

ABSTRACT

HCV is a heuristic attribute-based induction algorithm based on the newly-developed extension matrix approach. By dividing the positive examples (PE) of a specific class in a given example set into intersecting groups and adopting a set of strategies to find a heuristic conjunctive formula in each group which covers all the group's positive examples and none of the negative examples (NE), it can find a covering formula in form of variable-valued logic for PE against NE in low-order polynomial time. This paper presents the HCV algorithm in detail and provides a performance comparison of HCV with other inductive algorithms such as ID3 and AQ11.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
Bloedorn et al. 92
E. Bloedorn, R.S. Michalski, and J. Wnek, AQ17 - A Multistrategy Constructive Learning System, Reports of Machine Learning and Inference Laboratory, Center for Artificial Intelligence, George Mason University, USA, 1992.
 
Cestnik et al. 87
B. Cestnik, I. Kononenko, and I. Bratko, ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users, Progress in Machine Learning, I. Bratko and N. Lavrac (Eds.), Wilmslow: Sigma Press, England, 1987.
 
Clark et al. 89
 
Feigenbaum 81
E.A. Feigenbaum, Expert Systems in the 1980s, Infotech Stale of lhe Art Report on Machine Intelligence, A. Bond (Ed.), Maidenhead: Pergamon-Infotech, 1981.
 
Hong 85
J. Hong, AEI: An Extension Matrix Approximate Method for the General Covering Problem, International Journal of Computer and Information Sciences, 14(1985), 6: 421-437.
 
Hong et al. 87
J.R. Hong and C. Uhrik, The Extension Matrix Approach to Attribute-Based Learning, Progress in Machine Learning, I. Bratko and N. Lavrac (Eds.), Wilmslow: Sigma Press, England, 1987.
 
Hunt et al. 66
E.B. Hunt, J. Marin and P.T. Stone, Experiments in Induction, Academic Press, New York, 1966.
 
Michalski 75
R.S. Michalski, Variable-Valued Logic and Its Applications to Pattern Recognition and Machine Learning, Computer Science and Multiple- Valued Logic Theory and Applications, D.C. Rine (Ed.), Amsterdam: North-Holland, 1975, 506-534.
 
Michalski et al. 78
R.S. Michalski and J. Larson, Selection of Most Representative Training Exampies and Incremental Generation of VL1 Hypothesis- the Underlying Methodology and Description of Programs ESEL and AQll, Tech. Report UIUCDCS-R-78-867, Dept. of Computer Science, Univ. of Illinois at Champaign-Urbana, 1978.
 
Michalski et al. 86
R.S. Michalski, I. Mozetie, J. tlong and N. Lavrac, The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains, Proceedings of AAAI 1986, 1986, 1041-1045.
 
Quinlan 79
J.R. Quinlan, Discovering Rules by Induction from Large Collections of Examples, Introductory Readings in Expert Systems, D. Michie (Ed.), Gordon and Breach, London, 1979, 33--46.
 
Quinlan 86
 
Quinlan 89
J.R. Quinlan, Requirements for Knowledge Discovery in Databases, Proceedings of IJCAI- 89 Workshop on Knowledge Discovery in Databases, Detroit, USA, 1989, xiv.
 
Quinlan 92
 
Thrun et al. 91
S.B. Thrun, et al., The MONK's Problems- A Performance Comparison of Different Learning Algorithms, CMU-CS-gl-lgT, School of Computer Science, Carnegie Mellon University, 1991.
 
Utgoff 89
Valiant 84
 
Wu 92a
X. Wu, Optimization Problems in Extension Matrixes, Science in China, Series A, Chinese edition: 35(1992), 2: 200-207; English edition, 35(1992), 3: 363-373.
 
Wu 92b
X. Wu, HCV: A Heuristic Covering Algorithm for Extension Matrix Approach, DAI Research Paper No. 578, Department of Artificial Intelligence, University of Edinburgh, 1992.
 
Wu 92c
X. Wu, HCV User's Manual (Release 1.0 June 1992), DAI Technical Paper No. g, Department of Artificial intelligence, University of Edinburgh, 1992.
 
Wu 92d
X. Wu, Inductive Learning: Algorithms and Frontiers, Artificial Intelligence Review, 6(1992).