PS: Kyle, from a brief look, I would summarise it as sampling a small set
of KNN centroids.
On 19 June 2014 12:05, Joel Nothman <[email protected]> wrote:
> Hi Dayvid,
>
> Although it could potentially be included in scikit-learn, it looks like
> your components do not require modifying the existing codebase, and could
> be construed as an entirely independent project. This could be referenced
> from the Scikit-learn Wiki or similar, without having to decide whether
> these are within the scope (besides quality of code, testing, documentation
> and exemplification) for inclusion in the main project. Given the current
> burden on scikit-learn devs (e.g. 171 open pull requests), including a
> somewhat orthogonal set of algorithms (which may not be seminal enough for
> inclusion anyway) seems unlikely at present. I suggest you make the
> instance reduction directory a separate repository, and invite comment
> there.
>
> Cheers,
>
> - Joel
>
>
> On 19 June 2014 11:46, Dayvid Victor <[email protected]> wrote:
>
>> Hi Kyle,
>>
>> (sorry for the long answer).
>>
>> Instance Reduction techniques aims to reduce the amount of data
>> manipulated in order
>> to perform a classification/prediction ...
>>
>> Depending on the approach, they can remove noisy-data and outliers,
>> remove redundant data, generate new generalized data by combining existing
>> data. Undersampling, Oversampling, Hybrid-Sampling for Imbalanced Datasets.
>>
>> This figure is from a paper I was co-author 2 years ago, and I think it
>> shows what Instance Reduction does:
>>
>> https://dl.dropboxusercontent.com/u/23695780/asgp.png
>>
>>
>> These are two surveys that presents a taxonomy of prototype selection and
>> prototype generation, they do not have many citations (arround 100), but
>> they do reference the most important techniques and papers until 2012.
>>
>> - S. García, J. Derrac, J. R. Cano, and F. Herrera, “Prototype
>> selection for nearest neighbor classification: Taxonomy and empirical
>> study,” Pattern Analysis and Machine Intelligence, IEEE Transactions on,
>> vol. 34, no. 3, pp. 417–435, 2012.
>> - I. Triguero, J. Derrac, S. García, and F. Herrera, “A taxonomy and
>> experimental study on prototype generation for nearest neighbor
>> classification,” Systems, Man, and Cybernetics, Part C: Applications and
>> Reviews, IEEE Transactions on, vol. 42, no. 1, pp. 86–100, 2012
>>
>>
>> Actually, the idea is to contribute with the main ideas that handles:
>> High Noisy-Data, Outliers, (Edition, Condensation and Hybrid approaches);
>> Only the important techniques of each.
>>
>>
>> Thanks,
>>
>>
>> On Wed, Jun 18, 2014 at 2:45 PM, Kyle Kastner <[email protected]>
>> wrote:
>>
>>> Do you have any references for this technique? What is it typically used
>>> for?
>>>
>>>
>>> On Wed, Jun 18, 2014 at 12:26 PM, Dayvid Victor <[email protected]>
>>> wrote:
>>>
>>>> Hi there,
>>>>
>>>> Is anybody working on an Instance Reduction module for sklearn?
>>>>
>>>> I started working on those and I already have more than 10 IR (PS and
>>>> PG)
>>>> algorithms implemented (only 7 are in the repository right now);
>>>>
>>>> If anybody is working on this, I'd love to help you out; If not, but
>>>> anybody want to contribute to this module, please, reach me in my email!
>>>>
>>>>
>>>> https://github.com/dvro/scikit-learn/tree/instance_reduction/sklearn/instance_reduction
>>>>
>>>> Thanks,
>>>> --
>>>> Dayvid Victor R. de Oliveira
>>>> PhD Candidate in Computer Science at Federal University of Pernambuco
>>>> (UFPE)
>>>> MSc in Computer Science at Federal University of Pernambuco (UFPE)
>>>> BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
>>>> HPCC Systems Open Source Big Data Platform from LexisNexis Risk
>>>> Solutions
>>>> Find What Matters Most in Your Big Data with HPCC Systems
>>>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
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>>>>
>>>>
>>>
>>>
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>>> Find What Matters Most in Your Big Data with HPCC Systems
>>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
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>>
>>
>> --
>> *Dayvid Victor R. de Oliveira*
>> PhD Candidate in Computer Science at Federal University of Pernambuco
>> (UFPE)
>> MSc in Computer Science at Federal University of Pernambuco (UFPE)
>> BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
>>
>>
>> ------------------------------------------------------------------------------
>> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
>> Find What Matters Most in Your Big Data with HPCC Systems
>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
>> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
>> http://p.sf.net/sfu/hpccsystems
>> _______________________________________________
>> Scikit-learn-general mailing list
>> [email protected]
>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>
>>
>
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Find What Matters Most in Your Big Data with HPCC Systems
Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
Leverages Graph Analysis for Fast Processing & Easy Data Exploration
http://p.sf.net/sfu/hpccsystems
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