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RSFDGRC
2007
Springer
236views Data Mining» more  RSFDGRC 2007»
14 years 3 months ago
Constructing Associative Classifier Using Rough Sets and Evidence Theory
Constructing accurate classifier based on association rule is an important and challenging task in data mining. In this paper, a novel combination strategy based on rough sets (RST...
Yuan-Chun Jiang, Ye-Zheng Liu, Xiao Liu, Jie-Kui Z...
ICPR
2002
IEEE
14 years 11 months ago
Combining SVM Classifiers for Handwritten Digit Recognition
In this paper, we investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The cooperation schemes are applied on t...
Dejan Gorgevik, Dusan Cakmakov
ICPR
2002
IEEE
14 years 2 months ago
Analysis of Error-Reject Trade-off in Linearly Combined Classifiers
In this paper, a framework for the analysis of the error-reject trade-off in linearly combined classifiers is proposed. We start from a framework developed by Tumer and Ghosh [1,2...
Fabio Roli, Giorgio Fumera, Gianni Vernazza
ICML
2006
IEEE
14 years 10 months ago
Using query-specific variance estimates to combine Bayesian classifiers
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
Chi-Hoon Lee, Russell Greiner, Shaojun Wang
SSPR
2000
Springer
14 years 1 months ago
The Role of Combining Rules in Bagging and Boosting
To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are...
Marina Skurichina, Robert P. W. Duin