Feature Selection in MATLAB

Question

I have a dataset for text classification ready to be used in MATLAB. Each document is a vector in this dataset and the dimensionality of this vector is extremely high. In these cases peopl usually do some feature selection on the vectors like the ones that you have actually find the WEKA toolkit. Is there anything like that in MATLAB? if not can u suggest and algorithm for me to do it...? thanks

Answer 1

MATLAB (and its toolboxes) include a number of functions that deal with feature selection:

• RANDFEATURES (Bioinformatics Toolbox): Generate randomized subset of features directed by a classifier
• RANKFEATURES (Bioinformatics Toolbox): Rank features by class separability criteria
• SEQUENTIALFS (Statistics Toolbox): Sequential feature selection
• RELIEFF (Statistics Toolbox): Relief-F algorithm
• TREEBAGGER.OOBPermutedVarDeltaError, predictorImportance (Statistics Toolbox): Using ensemble methods (bagged decision trees)

You can also find examples that demonstrates usage on real datasets:

• Identifying Significant Features and Classifying Protein Profiles
• Genetic Algorithm Search for Features in Mass Spectrometry Data

In addition, there exist third-party toolboxes:

• Matlab Toolbox for Dimensionality Reduction
• LIBGS: A MATLAB Package for Gene Selection

Otherwise you can always call your favorite functions from WEKA directly from MATLAB since it include a JVM...

Answer 2

Feature selection depends on the specific task you want to do on the text data.

One of the simplest and crudest method is to use Principal component analysis (PCA) to reduce the dimensions of the data. This reduced dimensional data can be used directly as features for classification.

See the tutorial on using PCA here:

http://matlabdatamining.blogspot.com/2010/02/principal-components-analysis.html

Here is the link to Matlab PCA command help:

http://www.mathworks.com/help/toolbox/stats/princomp.html

Using the obtained features, the well known Support Vector Machines (SVM) can be used for classification.

http://www.mathworks.com/help/toolbox/bioinfo/ref/svmclassify.html http://www.autonlab.org/tutorials/svm.html

Answer 3

You might consider using the independent features technique of Weiss and Kulikowski to quickly eliminate variables which are obviously unimformative:

http://matlabdatamining.blogspot.com/2006/12/feature-selection-phase-1-eliminate.html