Database Reference
In-Depth Information
proportions appearing in the figure are representative of the proportions happening in
each step of the CT's building process, X c will be “sex” in the first step with branches
“m” and “f”, and “age” in the second one with 30 selected as cut value.
In the last step of the example in Fig. 1, the proposal is not to split the node in
two of the partitions and in another one the proposal is to split it using “color”
variable. If this proportion is maintained, the final decision will be to consolidate
the node as a leaf.
3 Experimental Methodology
Twenty databases of real applications have been used for the experimentation. Most
of them belong to the well known UCI Repository benchmark [2]. The Segment
domain has been used for experimentation in two different ways: taking into account
the whole set of data ( segment2310 ) and conserving the training/test division of the
original data set ( Segment210) . The Faithful database is a real data application from
our environment, centred in the electrical appliance's sector. Table 1 shows the wide
range of characteristics of the used domains: the number of patterns ( N. of patterns ) goes
from 148 to 24,507, the number of features ( N. of features ) from 4 to 57 and the
number of classes of the dependent variable ( N. of classes ) from 2 to 15.
Table 1. Description of experimental domains
Domain
N. of patterns
N. of features
N. of classes
Breast-W
699
10
2
Heart-C
303
13
2
Hypo
3163
25
2
Lymph
148
18
4
Credit-G
1000
20
2
Segment210
210
19
7
Iris
150
4
3
Glass
214
9
7
Voting
435
16
2
Hepatitis
155
19
2
Soybean-L
290
35
15
Sick-E
3163
25
2
Liver
345
6
2
Credit-A
690
14
2
Vehicle
846
18
4
Breast-Y
286
9
2
Heart-H
294
13
2
Segment2310
2310
19
7
Spam
4601
57
2
Faithful
24507
49
2
The CTC methodology has been compared to the C4.5 tree building algorithm
Release 8 of Quinlan, using the default parameter settings. Both kinds of trees have
been pruned, using the pruning algorithm of the C4.5 R8 software, to situate both
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