A Dynamic Committee Scheme on Multiple-Criteria Linear Programming Classification Method

Authors:
Meihong Zhu;Yong Shi;Aihua Li;Jing He
Affiliations:
CAS Research Center on Data Technology and Knowledge Economy, Management School, Graduate University of CAS, Beijing 100080, China and Statistics School, Capital University of Economics and Busine ...;CAS Research Center on Data Technology and Knowledge Economy, Management School, Graduate University of CAS, Beijing 100080, China;CAS Research Center on Data Technology and Knowledge Economy, Management School, Graduate University of CAS, Beijing 100080, China;CAS Research Center on Data Technology and Knowledge Economy, Management School, Graduate University of CAS, Beijing 100080, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Year:
2007

Citing 4
Cited 0

Bagging predictors

Machine Learning
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient progressive sampling

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Random Forests

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper aims to provide a scheme for effectively and efficiently finding an approximately optimal example size with respect to a given dataset when using Multiple-Criteria Linear Programming (MCLP) classification method. By integrating techniques of both progressive sampling and classification committee, it designs a dynamic classification committee scheme for MCLP. The experimental results have shown that our idea is feasible and the scheme is effective and efficient for exploring an approximately optimal sample size. The empirical results also help us to further investigate some general specialties of MCLP, such as the more general function expressions reflecting the relationship between accuracy and sample size, and between computing cost and sample size.