corral_augmented: Augmented corral data set: synthetic data with correlated...

Description Usage Format References Examples

Description

This is an artificial domain where the target concept is (X1^X2) V (X3^X4).
Data set by R. Kohavi. Training and test splits from SGI.
The first 6 features are the real features from the original corral data set. The rest are noise features added by V. Krakovna by shuffling copies of real features.
The SBFC paper uses subsets of this data set with the first 100 and 1000 features.

This is an artificial domain where the target concept is (X1^X2) V (X3^X4).
Data set by R. Kohavi. Training and test splits from SGI.
The first 6 features are the real features from the original corral data set. The rest are noise features added by Author by shuffling copies of real features.
The SBFC paper uses subsets of this data set with the first 100 and 1000 features.

Usage

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Format

TrainX

A matrix with 128 rows and 10000 columns.

TrainY

A vector with 128 rows.

References

SGI listing for corral data set

SBFC paper describing augmentation of corral data set

SGI listing for corral data set

Examples

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corral_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:6],
                                         TrainY = corral_augmented$TrainY))
corral100_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:100], 
                                  TrainY = corral_augmented$TrainY))
corral_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:6],
                                         TrainY = corral_augmented$TrainY))
corral100_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:100], 
                                  TrainY = corral_augmented$TrainY))

sbfc documentation built on May 29, 2017, 11:44 a.m.