The Golf/weather data used for the tables is the all nominal version
(weather.nominal.arff, as included with the Weka distribution). So, these examples are not
operating with numeric attributes.
In answer to your first question: the relief calculation used in this table is the version
that assumes attribute independence. It is given on page 58, and uses the Gini' index.
It applies to nominal attributes only. You can also take a look at Kononenko's paper
on Relief that describes this metric:
You should be able to use the contingency tables on page 11, along with the formulas on
page 58 to compute the values in table 4.2.
As for your second (and third) question: There is actually a typo in table 4.2 (I think) -
the correlation between temperature and humidity should be 0.258. The calculation for the
intercorrelation between outlook, temperature and humidity is (0.116 + 0.022 + 0.258) / 3
Hope this helps!
On 21/10/18, 8:37 PM, "Abdrahman0x" <wekalist-bounces(a)list.waikato.ac.nz on
behalf of info.abd.rahman(a)gmail.com> wrote:
Thank you Mark for your response, but am still little confused.
In your answer you had mentioned that CFS will be disretized for the numeric
attributes. Actually, in my dataset I have numeric attributes and the only
issue is with the class attributes which are nominal. I could apply the
internal Pearson Correlation to compute the correlation between the numeric
attributes (I used Data Analysis inside MS Excel for this purpose), the only
issue is how to compute the correlation with the nominal class attributes.
Inside your thesis, I found a good example (Table 4.2, and Table 4.3), as
your "Golf" dataset is something similar to my work (it has a nominal class
*(Question 1)* In your Table 4.2 (Page 72), you found the features
correlations between the attributes and between the class using "Relief",
but when I applied the Relief algorithm using Weka in my dataset, I got
confused about the output. Can you explain to me how did you get the class
attributes (Table 4.2 the class column) values using Relief. If you don't
mind explain to me the calculation steps to get the values of (0.130, 0.025,
0.185, 0.081) inside the table.
*(Question 2)* One more thing, in your Table 4.3 (Page 73), I understand the
(rff) column for the computation between two attributes which was calculated
in Table 4.3, but couldn't understand the same value when computed between 3
attributes; for examples between [Outlook Temperature Humidity] why the
value is 0.132 from where did you get this value?
*(Question 3)* Note, in your Table 4.3 (Page 73), the (rff) correlation
between [Temperature Humidity] you have written (0.258), I think it is
supposed to be (0.248) as shown in Table 4.2 (Page 72). Am I right or
wrong. Can you please explain.
I am sorry for my 3 long questions in my post, bur I am still a beginner and
would like to learn. I would appreciate your patient support.
Thank you so much in advance for your patience and for your support.
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