Re: [Clus-general] parameter tweaking

[Hendrik B. <hen...@cs...>]

Dear Martin,

I don't know if anyone has answered you yet.
I can't answer the pruning question.  
But regarding your question about learning with missing labels: 
- Learning from data with lots of missing labels is related to "semi-supervised learning".
- Most methods for semi-supervised learning essentially use the information that is in the distribution of the unlabeled instances to get improved performance.
- Such a setting can be mimicked in Clus by using VarianceReduction with variance being defined as a mix of variance in the predictor space and in the target space.
I believe there is a setting in Clus that allows you to list the attributes that should be used for the variance computation.
By including non-target attributes here, Clus will behave more like a semi-supervised learner, which means it may work better on your data.  No guarantee though - this much depends on the data.

Hope this helps.

Best,
Hendrik



On 15 Aug 2013, at 12:17, Martin Guetlein wrote:

> Hi,
> 
> First of all: hello to everybody on the list, and thanks to the
> authors of Clus to provide such a nice tool.
> 
> We are using Clus as one possible MLC method, applied to our
> toxicological dataset. The data has around 700 instances, around 25
> labels and lots (!) of missing labels.
> 
> Unfortunately Clus (and the other MLC) methods do not perform as good
> as we would like to (Clus predictions have only around 60 percent
> accuracy and auc).
> 
> Are there any settings especially suited for datasets with many
> missing labels? So far, I tried using GainRatio instead of
> VarianceReduction as heuristic.
> 
> I have disabled pruning, and it showed no effect (identical results
> with Pruning=C4.5). Is there an explanation for this?
> 
> Thanks and kind regards,
> Martin
> 
> 
> P.S.:
> I have adjusted the Clus source code to commons-math3-3.2 and
> weka-3-7-6, if s.o. is interested, I can share my changes
> 
> 
> -- 
> Dipl-Inf. Martin Gütlein
> Phone:
> +49 (0)761 203 8442 (office)
> +49 (0)177 623 9499 (mobile)
> Email:
> gue...@in...
> 
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