Research Output for Zijian Zheng
Note: The 2012, 2013 publications have not been audited.
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Publications
2000 School of Information Technology
Zheng, Z. (2000) Constructing conjunctive attributes using production rules, Journal of Research and Practice in Information Technology, vol. 0, no. 0, pp. 13-38, Australian Computer Society Inc., The Netherlands [C1]
ERA journal ID: Not matching ERA journal listno DRO entry yet for this publication
Zheng, Z. (2000) Constructing X-of-N Attributes for Decision Tree Learning, Machine Learning, vol. 0, no. 0, pp. 35-75, Kluwer Academic Publishers, The Netherlands [C1]
ERA journal ID: 18066 – Scopus EID: Not taggedZheng, Z. and Webb, G. (2000) Lazy learning of Bayesian rules, Machine Learning, vol. 0, no. 0, pp. 53-84, Kluwer Academic Publishers, Netherlands [C1]
ERA journal ID: 18066 – Scopus EID: Not taggedTing, K., Zheng, Z. and Webb, G. (2000) Learning lazy rules to improve the performance of classifiers, in Max Bramer, Ann Macintosh and Frans Coenen (eds), Proceedings of ES99: The 19th SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, pp. 122-131, Springer-Verlag, London [E1]
1999 School of Information Technology
Webb, Geoff., Wells, Jason. and Zheng, Zijian (1999) An experimental evaluation of integrating machine learning with knowledge acquisition , Machine Learning, vol. 0, no. 0, pp. 5-23, Kluwer Academic Press, Boston, MA, USA [C1]
ERA journal ID: 18066 – Scopus EID: Not taggedTing, K. and Zheng, Z. (1999) Improving the performance of boosting for naive Bayesian classification, in N. Zhong & L. Zhou (eds), 3rd Pacific Asia Conference on knowledge discovery and data mining, pp. 296-305, Springer-Verlag, Berlin [E1]
Zheng, Z. and Low, B.* (1999) Classifying Unseen Cases with Many Missing Values, in Ning Zhong and Lizhu Zhou (eds), "Methodologies for knowledge discovery and data mining', pp. 370-374, Springer, Berlin [E1]
Zheng, Z. and Webb, G. (1999) Stochastic attribute selection committees with multiple boosting: learning more accurate and more stable classifier committees, in Ning Zhong and Lizhu Zhou (eds), 'Methodologies for knowledge discovery and data mining' 3rd Pacific-Asia Conference, pp. 123-132, Springer, Berlin [E1]
1999 External
Zheng, Z.*, Webb, G. and Ting, K. (1999) Lazy Bayesian Rules: A lazy semi-naive Bayesian learning technique competitive to boosting decision trees, in Ivan Bratko and Saso Dzeroski (eds), Machine learning, proceedings of the 16th international conference, pp. 493-502, Morgan Kaufmann, California [E1]
1998 School of Information Technology
Zheng, Z. (1998) A Comparison of Constructing Different Types of New Feature for Decision Tree Learning, "Feature Extraction, Construction and Selection: a Data Mining Perspective", pp. 239-255, Kluwer Academic Publishers, "Norwell, Massachusetts, USA" [B1]
Zheng, Z. (1998) Constructing Conjunctions Using Systematic Search on Decision Trees, Knowledge-Based Systems, vol. 0, no. 0, pp. 421-430, Elsevier Science B.V., "Amsterdam, Netherlands" [C1]
ERA journal ID: Not matching ERA journal listTing, K. and Zheng, Z. (1998) Boosting Cost-Sensitive Trees, First International Conference on Discovery Science, pp. 244-255, Springer-Verlag, "Berlin, Germany" [E1]
Ting, K.* and Zheng, Z. (1998) Boosting Trees for Cost-Sensitive Classifications, 10th European Conference on Machine Learning, pp. 190-195, Springer-Verlag, Berlin [E1]
Zheng, Z. and Webb, G. (1998) Stochastic Attribute Selection Committees, 11th Australian Joint Conference on Artificial Intelligence, pp. 321-332, Springer-Verlag, "Berlin, Germany" [E1]
Zheng, Z., Webb, G. and Ting, K. (1998) Integrating Boosting and Stochastic Attribute Selection Committees for Further Improving the Performance of Decision Tree Learning, 10th International Conference on Tools With Artificial Intelligence TAI '98, pp. 216-223, IEEE Computer Society, "Los Alamitos, USA" [E1]
Zheng, Z. and Webb, G. (1998) Multiple Boosting: A Combination of Boosting and Bagging, The International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 1133-1140, CSREA, USA [E1]
Zheng, Z. (1998) Scaling Up the Rule Generation of C4.5, Research and Development in Knowledge Discovery and Data Mining, pp. 348-359, Springer-Verlag, "Berlin, Germany" [E1]
Zheng, Z. (1998) Naive Bayesian Classifier Committees, 10th European Conference on Machine Learning, pp. 196-207, Springer-Verlag, "Berlin, Germany" [E1]
Zheng, Z. (1998) Generating Classifier Committees by Stochastically Selecting Both Attributes and Training Examples, 5th Pacific Rim International Conference on Artificial Intelligence, pp. 12-23, Springer-Verlag, "Berlin, Germany" [E1]
1997 School of Information Technology
Chiu, B., Webb, G. and Zheng, Z. (1997) Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions., Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97), pp. 349-358, Springer-Verlag, Berlin [E1]
Zheng, Z. (1997) Constructing Conjunctions using Systematic Search on Decision Trees, Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 88-101, World Scientific, Singapore [E1]
1996 School of Information Technology
Zheng, Z. (1996) Effects of Different Types of New Attribute on Constructive Induction, Proceedings of Eighth IEEE International Conference on Tools with Artificial Intelligence, pp. 254-257, IEE Computer Society Press, California, USA [E1]
Grants and Consultancies
All years
Research Income - National Competitive Grants
Webb, G, Pazzani, M* and Zheng, Z*. Learning efficient accurate Bayesian classifiers from data, Australian Research Council Large Grant [T]
- 2000: $56,971
Higher Degree by Research Completions supervised by Zijian Zheng
No completions found or audited.
- Legend:
- * Not a member of Deakin University at time of output.
DRO to publications collection last synchronised: Monday 20th May 2013 10:04pm
