Database Reference
In-Depth Information
Table 5.
Results for the CBF data set.
10
20
30
40
50
60
70
80
90
100
Error/Number of Literals
Points
8.67
6.02
5.64
4.51
4.39
3.64
3.64
3.15
3.02
3.51
Intervals
3.63
1.64
1.00
0.75
0.75
0.88
0.50
0.75
0.50
0.50
Error/Series Length Percentage
Points
67.17
48.15
35.85
27.10
10.13
4.51
3.39
4.26
3.77
3.51
Intervals
67.19
53.78
34.34
15.44
5.04
0.88
0.88
0.75
0.38
0.50
Table 6.
Results for the CBF-Var data set.
10
20
30
40
50
60
70
80
90
100
Error/Number of literals
Points
8.75
5.24
4.37
3.25
4.01
3.98
3.99
4.00
4.12
3.49
Intervals
4.11
2.99
2.13
1.75
1.50
1.25
1.13
1.25
1.13
1.13
Error/Series length percentage
Points
68.30
49.02
37.99
22.22
5.62
4.11
3.37
3.49
3.49
3.49
Intervals
65.84
54.38
32.83
16.67
3.25
1.61
1.00
1.00
1.13
1.13
The error reported in [Kadous (1999)] is 1.9, using event extraction,
event clustering and decision trees. Our results using interval based literals
are better than this value using 20 or more literals. Moreover, our method
is able to obtain a better result than 1.9, 0.88, using early classification
with 100 literals when only 60% of the series length is available.
Variable Length Version. All the examples of the CBF data set have
the same length. In order to check the method for variable length series, the
data set was altered. For each example, a random number of values were
deleted from its end. The maximum number of values eliminated is 64,
the half of the original length. The obtained results for this new data set,
named CBF-Var , are shown in Figure 4(B) and in Table 6. When using a
variable length data set and early classification, the series length percentage
in graphs and tables are relative to the length of the longest series in the
data set. For instance, in this data set 25% corresponds to the first 32 points
of each series, because the longest series have 128 points.
An interesting issue is that the obtained results with this altered data
set are still better than the results reported in [Kadous (1999)] using the
original data set.
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