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» Mining Several Data Bases with an Ensemble of Classifiers
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ICDM
2003
IEEE
109views Data Mining» more  ICDM 2003»
14 years 29 days ago
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
We experimentally evaluate randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches co...
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfi...
KES
2006
Springer
13 years 7 months ago
Evolutionary Tuning of Combined Multiple Models
Abstract. In data mining, hybrid intelligent systems present a synergistic combination of multiple approaches to develop the next generation of intelligent systems. Our paper prese...
Gregor Stiglic, Peter Kokol
MCS
2007
Springer
14 years 1 months ago
Selecting Diversifying Heuristics for Cluster Ensembles
Abstract. Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ...
Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
CP
2010
Springer
13 years 5 months ago
Ensemble Classification for Constraint Solver Configuration
The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine lea...
Lars Kotthoff, Ian Miguel, Peter Nightingale
PAKDD
2005
ACM
114views Data Mining» more  PAKDD 2005»
14 years 1 months ago
Increasing Classification Accuracy by Combining Adaptive Sampling and Convex Pseudo-Data
The availability of microarray data has enabled several studies on the application of aggregated classifiers for molecular classification. We present a combination of classifier ag...
Chia Huey Ooi, Madhu Chetty