Database Reference
In-Depth Information
95.0%
88.5%
90.0%
85.0%
80.0%
76.1%
75.0%
70.0%
months 1 to 5
validated by
month 6
month 1
validated by
month 6
month 5
validated by
month 6
Fig. 3. CD confidence level (1
−
p
-value) outcomes of validating the sixth month on the
fifth and the first month in 'Manufacturing' database.
procedure. No results of the changed detection metric (CD) exceeding
the 95% confidence level were produced in any period. This means that
no “false alarms” were issued by the procedure.
•
Statistically significant changes in the distributions of the candidate
input (independent) variables and the target (dependent) variable across
monthly intervals have not generated a significant change in the rules,
which are induced from the database.
•
The CD metric implemented by our method can also be used to determine
whether an incrementally built model is stable. If we are applying a stable
data mining algorithm, like the Info-Fuzzy Network, to an accumulated
amount of data, it should produce increasing confidence levels of the CD
metric over the initial periods of the time series, as more data supports
the induced classification model.
Thus the results obtained from a real-world time series database confirm
the conclusions of the experiments on artificial datasets with respect to
reliability of the proposed change detection methodology.
5. Conclusions and Future Work
As mentioned above, most methods of batch learning are based on the
assumption that the training data involved in building and verifying the
