By J. A. Bergstra, J. Heering, P. Klint
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Additional info for Algebraic Specification (Acm Press Frontier Series)
EXPERIMENTAL SETTING 45 In the next chapter we design and propose ADWIN, a change detector and predictor with these characteristics, using an adaptive sliding window model. ADWIN’s window management strategy will be to compare all the adjacent subwindows in which is possible to partition the window containing all the data. It seems that this procedure may be the most accurate, since it looks at all possible subwindows partitions. On the other hand, time cost is the main disadvantage of this method.
Of data items whose distribution varies over time in an unknown way. The outputs of the algorithm are, at each time step • an estimation of some important parameters of the input distribution, and • a signal alarm indicating that distribution change has recently occurred. We consider a speciﬁc, but very frequent case, of this setting: that in which all the xt are real values. 3. A METHODOLOGY FOR ADAPTIVE STREAM MINING 41 such as the variance. The only assumption on the distribution is that each xt is drawn independently from each other.
The basic idea of the drift detection method is to control this error-rate. If the distribution of the examples is stationary, the error rate of Na¨ıve-Bayes decreases. If there is a change on the distribution of the examples the Na¨ıve Bayes error increases. 1. When it detects an statistically signiﬁcant increase of the Na¨ıve-Bayes error in a given node, an indication of a change in the distribution of the examples, this suggest that the splitting-test that has been installed at this node is no longer appropriate.
Algebraic Specification (Acm Press Frontier Series) by J. A. Bergstra, J. Heering, P. Klint