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Machine Learning: Feature Extraction vs Feature Selection in Java  RSS feed

 
Greenhorn
Posts: 22
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I am new in Machine Learning and developing a sample application having around 100 attribute using java(android device).
I am slightly confused as to what "Feature Selection" and "Feature Extraction" .
What I studied from sources is

Feature Extraction :-Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. When performing analysis of complex data one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computation power or a classification algorithm which overfills the training sample and generalizes poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy.

Feature Selection:- Feature Selection methods choose features from the original set based on some criteria, Information Gain, Correlation and Mutual Information are just criteria that are used to filter out unimportant or redundant features. Embedded or wrapper methods, as they are called, can use specialized classifiers to achieve feature selection and classify the data set at the same time.


I just want to know that how can extract the feature using java code ? is there any standard library like weka or Java ML or some other provide facilities for feature Extraction. ?

or We have to develop our own algorithms .

I have read PCA and SVD are used for Feature Extraction . How can I use them in my Java Code.

Sorry if I have write any misleading term.


I don't know this is right forum to post my query for machine learning , please move it in appropriate forum if this is incorrect forum.


Thanks,
Raghvendra
 
Rancher
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Your question is very generic. What is the exact problem at hand? What are the inputs, and what are the outputs you'd like?
 
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