Databases Reference
In-Depth Information
5 Experiments
In our research we used the same data sets that were used for experiments
in [9]. All of these data set have numerical attributes and are completely spec-
ified (i.e., for every attribute and every case the corresponding attribute value
is specified). These ten data sets are presented in Table 2. For experiments
we used three different approaches: the original LEM2 algorithm with dis-
cretization based on entropy as preprocessing, and two versions of MLEM2
algorithms. The first version of MLEM2 was not equipped with a mechanism
for merging intervals within the same rule. For example, from
pima
data set,
a typical induced rule was:
6, 38, 38
(Diabetes, 0.078..0.2995) & (Pressure, 57..122) &
(Diabetes, 0.1655..2.42) & (Age, 21..38.5) &
(Pressure, 0..83) & (Glucose, 0..99.5) -
>
(Class, 0)
It is clear that two conditions, both associated with the same attribute
Diabetes
, namely:
(Diabetes, 0.078..0.2995) and (Diabetes, 0.1655..2.42)
can be merged into one condition:
(Diabetes, 0.1655..0.2995).
Similarly, for attribute
Pressure
, the following two conditions
(Pressure, 57..122) and (Pressure, 0..83)
can be also merged into one condition:
Pressure, 57..83).
The third way to induce rules was the newest version of the MLEM2 al-
gorithm that is able to merge conditions with intervals. We used results of
Tabl e 2 .
Data sets
Data set
Number of
Cases
Attributes
Concepts
Bank
66
5
2
Bricks
216
10
2
Bupa
345
6
2
Buses
76
8
2
German
1
,
000
24
2
Glass
214
9
6
HSV
122
11
2
Iris
150
4
3
Pima
768
8
2
Segmentation
210
19
7
