Databases Reference
In-Depth Information
Tabl e 5 .
Accuracy
Data set
Discretization
MLEM2 without MLEM2 with
based on entropy
merging
merging
and LEM2
conditions
conditions
Bank
97
95
95
Bricks
92
92
87
Bupa
66
65
64
Buses
99
96
93
German
74
70
69
Glass
67
72
69
HSV
56
60
65
Iris
97
95
94
Pima
74
71
70
Segmentation
64
89
84
6 Conclusions
To compare our three different approaches to rule induction from numerical
data the Wilcoxon matched-pairs signed rank test was used (with level of
significance 5%, two-tailed test) [11]. All three approaches were compared
pair wise. The total number of rules is the largest for discretization based
on entropy, used as preprocessing for original data sets, and then the LEM2
algorithm for rule induction. For both versions of MLEM2 the difference in
performance is - statistically - insignificant.
Similarly, for the total number of conditions in the induced rule sets, the
worst result - the largest number of conditions - was induced by the first ap-
proach: discretization based on entropy and then the LEM2 algorithm for rule
induction. The performance of the MLEM2 algorithm with merging intervals
was better than MLEM2 without merging intervals, as expected.
Surprisingly, all three approaches show no significant difference in perfor-
mance for the most important parameter: accuracy.
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