[scikit-learn] suggested classification algorithm

[Sebastian Raschka]

Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning-imbalanced-classes/ 


> On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith at macys.com> wrote:
> 
> Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
>  
>  
> __________________________________________________________________________________________________________________________________________
> Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 
> 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith at macys.com
>  
> From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com at python.org] On Behalf Of Fernando Marcos Wittmann
> Sent: Wednesday, November 16, 2016 3:11 PM
> To: Scikit-learn user and developer mailing list
> Subject: Re: [scikit-learn] suggested classification algorithm
>  
> ⚠ EXT MSG:
> Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique (http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations. 
>  
> On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3 at gmail.com> wrote:
> Greetings,
>  
> I want to design a program that can deal with classification problems of the same type, where the  number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
>  
> http://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png
>  
> However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
>  
> thanks in advance
> Thomas
>  
>  
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> ======================================================================
> Thomas Evangelidis
> Research Specialist
> CEITEC - Central European Institute of Technology
> Masaryk University
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> email: tevang at pharm.uoa.gr
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> Fernando Marcos Wittmann
> MS Student - Energy Systems Dept. 
> School of Electrical and Computer Engineering, FEEC
> University of Campinas, UNICAMP, Brazil
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