CHANGE DETECTION IN CLASSIFICATION MODELS INDUCED FROM TIME SERIES DATA - Data Mining in Time Series Databases - page 101

CHAPTER 5
CHANGE DETECTION IN CLASSIFICATION MODELS
INDUCED FROM TIME SERIES DATA
Gil Zeira ∗ and Oded Maimon †
Department of Industrial Engineering
Tel-Aviv University, Tel-Aviv 69978, Israel
E-mail: ∗ [e-mail]; † [e-mail]
Mark Last
Department of Information Systems Engineering
Ben-Gurion University, Beer-Sheva 84105, Israel
E-mail: [e-mail]
Lior Rokach
Department of Industrial Engineering
Tel-Aviv University, Tel-Aviv 69978, Israel
E-mail: [e-mail]
Most classification methods are based on the assumption that the
historic data involved in building and verifying the model is the best
estimator of what will happen in the future. One important factor that
must not be set aside is the time factor. As more data is accumulated
into the problem domain, incrementally over time, one must examine
whether the new data agrees with the previous datasets and make the
relevant assumptions about the future. This work presents a new change
detection methodology, with a set of statistical estimators. These changes
can be detected independently of the data mining algorithm, which is
used for constructing the corresponding model. By implementing the
novel approach on a set of artificially generated datasets, all significant
changes were detected in the relevant periods. Also, in the real-world
datasets evaluation, the method produced similar results.
Keywords : Classification; incremental learning; time series; change
detection; info-fuzzy network.
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