Hi Mark, 

Thank for the useful reply. 

I have the following issues if you don't mind:

a) In the "Select attributes" tab, does feature selection techniques are: 
Chi-Square and Information Gain Ratio? And feature extraction techniques are: Principal Component Analysis and Latent Semantic? Or all they are performing attribute selection?

b) When using FilteredClassifier where the "classifier" parameter is "AttributeSelectedClassifierwith "PrincipalComponents" as an evaluator. And the filter parameter is also "PrincipalComponents". As you can see the PrincipalComponents is applied two times, so in this case, are we performing feature extraction two times? 

Thanks in advance,
Fernando

On Tue, Sep 12, 2017 at 5:45 AM, Mark Hall <mhall@waikato.ac.nz> wrote:
These methods perform feature extraction. Because each extracted feature has a "merit" score associated with it, a subset of transformed features can be selected by applying a threshold to the ranked list (via the Ranker search method) or by selecting the top N from the list. You can right click over an entry in the history list and select "Visualize transformed data" to view a scatterplot. From the scatterplot window you can save the transformed data if desired.

Cheers,
Mark.

On 8/09/17, 3:38 PM, "Fernando Lavin" <wekalist-bounces@list.waikato.ac.nz on behalf of fernandolavin78@gmail.com> wrote:

    Hi all,
    I have confusion with the functionality of some methods in the "Select attributes" tab, especially Principal Component Analysis and Latent Semantic Analysis

    Hence, my question:

    a) Does "Principal Component Analysis" method apply feature extraction in the "Select Attributes" tab? Or it performs different functionality that is mostly related to feature selection?

    b) How about "Latent Semantic Analysis" in the "Select attributes" tab, does it perform feature extraction? Or it performs a functionality that is mainly related to feature selection?

    Thanks in advance.
    Fernando


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