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Question: When we were training the anfis system, does over-fitting occur when we have a large epoch number instead of when we have large membership function number? Over-fitting =

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slide1

Question:

When we were training the anfis system, does over-fitting occur when we have a large epoch number instead of when we have large membership function number?

Over-fitting=

the result when anfis automatically reduces training error in each epoch, making the system super-fits the training data, but may not fit other data in general

slide2

Our best fis system is the one called “ampFisUnfiltered3.fis” which uses the following criteria:

  • Unfiltered data
  • Number of membership function=3
  • Type of membership function=‘gbellmf’
  • epoch_n=20
slide3

Using the following code, we can estimate temperatures if we know avgAmp(average amplitude, our measured feature)

  • fis=readfis('ampFisUnfiltered3.fis‘)
  • temp_eval=evalfis(avgAmp,fis)
slide7

E.g

avgAmp=0.0200018485069275

temp_eval=evalfis(avgAmp,fis)

>>> temp_eval=81.4416

slide8

However, the true temperature is 70.7378,

  • We have an error of

81.4416-70.7378=10.7038

Which is considerably big, about 15% of true value.

Could it be that our fis, “ampFisUnfiltered3.fis” is not a good model to predict this relationship between avgAmp and temperature?

presentableData.xlsx

slide11

right Skew - the mass of the distribution is concentrated on the left of the figure. It has relatively few high values

Also referred to as positive skew

slide12

Another thing to be noticed, exactly 105 out of the 130 big-error data points are measured under stressed condition (elevated temperature)

  • this might suggest:

Average amplitude of chicken vocalization as a feature of stress level works well under low-stress environment, but produces wider ranges of fluctuation under high stressed ones.

changing the type of membership function
Changing the type of membership function
  • This time we change

Type of membership function=‘gaussmf’

While holding other variables constant

Let’s see the behavior of this new fis system:

‘ampFisUnfiltered3Gaussmf.fis’

mfComparedataUnStressed.xlsx

mfComparedataStressed.xlsx

slide14

The average error and standard deviation of error vary within the two fis systems

  • However, after calculating the Mean Square Error for both sytems, both stressed and unstressed
slide15

The smaller the MSE, the better the fis system

  • You can see that although very similar, it’s hard to tell which one is a better parameter, “gbellmf” or “gaussmf” since their corresponding MSE value varies for stressed and unstressed
  • Also the errors in unstressed groups are a lot smaller than those generated by stressed groups, which again proves our point saying “Average amplitude of chicken vocalization as a feature of stress level works well under low-stress environment, but produces wider ranges of fluctuation under high stressed ones.”