Re: [gplab-users] classification of 120 length by 30 cases matrix

[Sara S. <sa...@de...>]

Dear James,

Sorry for the delay in replying. I am currently writing my PhD thesis so 
I am VERY busy. But I will try to help.

You say that GPLAB does converge. Does it mean that it reaches an 
optimal solution according to your fitness function?

Also, I cannot imagine what type of problem you are having in 
interpreting the final tree. Can you give an example?

Cheers,
Sara


James Griffin wrote:

> I am currently having problems to get a useful output from the GPlab. I 
> realise this is due to my understanding of the problem and wondered if 
> anyone could help me get started on this. I have some useful ideas and 
> already classified my data using fuzzy clustering. To that, I would like 
> to classify the data using GP as I have worked on this for the last 4 
> months and don’t really want to say it can’t classify the data when in 
> fact it’s down to my understanding and not the strategy behind GP.
> The problem:
> 
> If I have 30 cases of data - for argument sake, the 1st ten represent 
> classification phenomenon 1, the next ten (11-20) represent 
> classification phenomenon 2 and lastly, the last ten (21-30), represent 
> classification phenomenon 3. In short I wish to segregate classes 1 to 3 
> using ‘if,’ ‘else,’ ‘if greater than,’ and ‘less than rules.’ I use two 
> text inputs one with the data (120*30 matrix) and one with the targets 1 
> to 3.  I have got useful rules from tree regression software (Matlab) 
> however not GP, I believe it’s my fitness function (standard GP toolbox) 
> and terminals are currently set to nil. The functions are as stated 
> above (implemented my own ‘min,’ ‘max,’ kurtosis,’ ‘stddev,’ although 
> these just confused matters.
> 
> The GP lab is converging however I am having problems in interpreting 
> the output string/tree for classifying the 3 classes of data (it would 
> be useful to get an output similar to the NN GP output against desired 
> output. Each of the 30 cases are 120 pts in size and each of the classes 
> are significantly different from the other classes. I.e. first class has 
> first 30 pts larger than the 2nd batch and the 3rd batch has 60 -90 pts 
> both larger than the other classes.
> 
> Based on this problem I would like to use the fitness measure to 
> continually update a correct classification and penalize a 
> misclassification. Perhaps a figure of merit could be associated to 
> ensure the best individual is found. Can anyone help me getting started 
> with such problem? I.e what functions and terminals do they think are 
> appropriate (I am looking at le, gr and myif), not sure what terminals 
> to use ??? The fitness function should look if the rules/tree segregates 
> the data giving the correct classification .....to this end, I believe 
> something similar to the ant demo is required but not as complex.
> 
> I would like to express my thanks to Sara for producing such a user 
> friendly and powerful tool box and once I get into this in greater 
> detail I would like to share some ideas I have for enabling your GP 
> toolbox to handle n-dimensionally huge data ??? Although automating may 
> take some effort  and that's of course if you haven't already done this!
> Again I would be grateful for any help any of the GP community can give 
> as I have been mucking around with the toolbox for 2 months now and I 
> can't seem to replicate John Koza/Daniel Howards GP classification ideas 
> (they classify imagery data - although mine is mechanical measurements 
> and Acoustic Emission and a lot smaller in size due to n-dimensional 
> reduction).
> 
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