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IJON
2006
146views more  IJON 2006»
13 years 10 months ago
Feature selection and classification using flexible neural tree
The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator se...
Yuehui Chen, Ajith Abraham, Bo Yang
LION
2010
Springer
209views Optimization» more  LION 2010»
14 years 3 months ago
Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression
We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimiza...
Kalyan Veeramachaneni, Katya Vladislavleva, Una-Ma...
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
14 years 3 months ago
A genetic algorithm for learning significant phrase patterns in radiology reports
Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. R...
Robert M. Patton, Thomas E. Potok, Barbara G. Beck...
GIS
2009
ACM
14 years 2 months ago
Classification of raster maps for automatic feature extraction
Raster maps are widely available and contain useful geographic features such as labels and road lines. To extract the geographic features, most research work relies on a manual st...
Yao-Yi Chiang, Craig A. Knoblock
SAC
2006
ACM
14 years 4 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal