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

[James G. <jam...@ho...>]

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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