Feature Selection using Matlab.



The DEMO includes 5 feature selection algorithms:

• 　　Sequential Forward Selection (SFS)

• 　　Sequential Floating Forward Selection (SFFS)

• 　　Sequential Backward Selection (SBS)

• 　　Sequential Floating Backward Selection (SFBS)

• 　　ReliefF



Two CCR estimation methods:

• 　　Cross-validation

• 　　Resubstitution



After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.



Figure: Upper panel is the pattern x feature matrix

Lower panel left are the features selected

Lower panel right is the CCR curve during feature selection steps

Right panel is the classification results of some patterns.



This software was developed using Matlab 7.5 and Windows XP.



Copyright: D. Ververidis and C.Kotropoulos

AIIA Lab, Thessaloniki, Greece,

jimver@aiia.csd.auth.gr

costas@aiia.csd.auth.gr-Feature Selection using Matlab.



The DEMO includes 5 feature selection algorithms:

• 　　Sequential Forward Selection (SFS)

• 　　Sequential Floating Forward Selection (SFFS)

• 　　Sequential Backward Selection (SBS)

• 　　Sequential Floating Backward Selection (SFBS)

• 　　ReliefF



Two CCR estimation methods:

• 　　Cross-validation

• 　　Resubstitution



After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.



Figure: Upper panel is the pattern x feature matrix

　　　　　　 Lower panel left are the features selected

　　　　　　 Lower panel right is the CCR curve during feature selection steps

　　　　　　 Right panel is the classification results of some patterns.



This software was developed using Matlab 7.5 and Windows XP.



Copyright: D. Ververidis and C.Kotropoulos

　　　　　　　　 AIIA Lab, Thessaloniki, Greece,

　　　　　　　　 jimver@aiia.csd.auth.gr

　　　　　　　　 costas@aiia.csd.auth.gr