Dear Weka Users/Developers,
I'm trying to do serial feature selection using attributeselectedclassifier in a nested manner where the first selector is a ranker and the second one is a wrapper (linearforwardselection).
I am assuming that numusedattributes (wrapper) corresponds to k. Therefore, in fixed-set configuration, I expect numtoselect (ranker) not to play any role as long as it is greater than or equal to numusedattributes. However, I am getting very dissimilar results when I change numtoselect. It seems like I am seriously confused about either linearforwardselection or attributeselectedclassifier. Hope it is not both :-).
Thank you very much in advance,
Huseyin
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weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.InfoGainAttributeEval " -S "weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N -1" -W weka.classifiers.meta.AttributeSelectedClassifier -- -E "weka.attributeSelection.WrapperSubsetEval -B weka.classifiers.functions.LibSVM -F 5 -T 0.01 -R 1 -E DEFAULT -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\\ProgramData\\Weka-3-8 -seed 1" -S "weka.attributeSelection.LinearForwardSelection -D 0 -N 5 -I -K 5 -T 0" -W weka.classifiers.functions.LibSVM -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\ProgramData\Weka-3-8 -seed 1
weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.InfoGainAttributeEval " -S "weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N 5" -W weka.classifiers.meta.AttributeSelectedClassifier -- -E "weka.attributeSelection.WrapperSubsetEval -B weka.classifiers.functions.LibSVM -F 5 -T 0.01 -R 1 -E DEFAULT -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\\ProgramData\\Weka-3-8 -seed 1" -S "weka.attributeSelection.LinearForwardSelection -D 0 -N 5 -I -K 5 -T 0" -W weka.classifiers.functions.LibSVM -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\ProgramData\Weka-3-8 -seed 1
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I'm trying to do serial feature selection using attributeselectedclassifier in a nested manner where the first selector is a ranker and the second one is a wrapper (linearforwardselection).
I am assuming that numusedattributes (wrapper) corresponds to k. Therefore, in fixed-set configuration, I expect numtoselect (ranker) not to play any role as long as it is greater than or equal to numusedattributes. However, I am getting very dissimilar results when I change numtoselect. It seems like I am seriously confused about either linearforwardselection or attributeselectedclassifier. Hope it is not both :-).
Thank you very much in advance,
Huseyin
***
weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.InfoGainAttributeEval " -S "weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N -1" -W weka.classifiers.meta.AttributeSelectedClassifier -- -E "weka.attributeSelection.WrapperSubsetEval -B weka.classifiers.functions.LibSVM -F 5 -T 0.01 -R 1 -E DEFAULT -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\\ProgramData\\Weka-3-8 -seed 1" -S "weka.attributeSelection.LinearForwardSelection -D 0 -N 5 -I -K 5 -T 0" -W weka.classifiers.functions.LibSVM -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\ProgramData\Weka-3-8 -seed 1
weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.InfoGainAttributeEval " -S "weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N 5" -W weka.classifiers.meta.AttributeSelectedClassifier -- -E "weka.attributeSelection.WrapperSubsetEval -B weka.classifiers.functions.LibSVM -F 5 -T 0.01 -R 1 -E DEFAULT -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\\ProgramData\\Weka-3-8 -seed 1" -S "weka.attributeSelection.LinearForwardSelection -D 0 -N 5 -I -K 5 -T 0" -W weka.classifiers.functions.LibSVM -- -S 0 -K 2 -D 1 -G 0.01 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -Z -B -model C:\ProgramData\Weka-3-8 -seed 1
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