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DMIN
2006
158views Data Mining» more  DMIN 2006»
15 years 6 months ago
Ensemble Selection Using Diversity Networks
- An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion t...
Qiang Ye, Paul W. Munro
ESWA
2006
165views more  ESWA 2006»
15 years 5 months ago
Optimal ensemble construction via meta-evolutionary ensembles
In this paper we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to ...
YongSeog Kim, W. Nick Street, Filippo Menczer
GFKL
2004
Springer
117views Data Mining» more  GFKL 2004»
15 years 10 months ago
Cluster Ensembles
Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package˜c...
Kurt Hornik
DEXAW
1999
IEEE
97views Database» more  DEXAW 1999»
15 years 9 months ago
Mining Several Data Bases with an Ensemble of Classifiers
The results of knowledge discovery in databases could vary depending on the data mining method. There are several ways to select the most appropriate data mining method dynamicall...
Seppo Puuronen, Vagan Y. Terziyan, Alexander Logvi...
WAIM
2004
Springer
15 years 10 months ago
An Empirical Study of Building Compact Ensembles
Abstract. Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble...
Huan Liu, Amit Mandvikar, Jigar Mody