Discretize data into categories

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Discretize data into categories

mkarmi
Hi all,

I need a help in discretizing the data into categorical data ( over expression, baseline, under-expression) using the standard deviation and the mean. Can anyone guide me by steps. Shall I use supervised or unsupervised discretization?

Thank you

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Re: Discretize data into categories

Eibe Frank-3
You can implement that by combining

with the NumericToNominal filter and finally RenameNominalValues.

Here is a possible configuration of MathExpression:

  weka.filters.unsupervised.attribute.MathExpression -E "ifelse(A<MEAN-1.96*SD,-1,ifelse(A>MEAN+1.96*SD,1, 0))"

This will replace all values of an attribute A that are smaller than

   mean_of_A - 1.96 * standard_deviation_of_A

with the value -1, all values of A that are greater than

  mean_of_A + 1.96 * standard_deviation_of_A

with the value 1, and all other values of A with value 0.

Cheers,
Eibe

On Sat, Dec 23, 2017 at 2:48 AM, Murad Al-Rajab <[hidden email]> wrote:
Hi all,

I need a help in discretizing the data into categorical data ( over expression, baseline, under-expression) using the standard deviation and the mean. Can anyone guide me by steps. Shall I use supervised or unsupervised discretization?

Thank you

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Re: Discretize data into categories

Abdrahman0x
Hi...

I found in a paper that the authors had pre-processed the data so each
attribute has zero mean value and unit variance. Then they also discretized
the data into categorical data so that each attribute expression variable
using the respective σ (standard deviation) and μ (mean).
I had used the unsupervised "Standardize" and "MathExpression" as
multifilter inside the FilteredClassifier in the classify panel, but got
results which are so far a different from those presented in the paper
though I am using the same data set.

Can anyone explain to me how to implement such pre-processing in the correct
manner inside Weka as I am afraid that the way I am using is not correct.

Thank you,
AR



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