Essay on Identifying The Concept Of Sliding Window

Essay on Identifying The Concept Of Sliding Window

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i. Windowing techniques
Windowing technique uses the concept of sliding window. This is well liked tactic to hand out with time emerging data. Sliding Window give permission to limiting the samples which is going to learn as well as it flush out the old data and add new data, so the paradigm gain the newest idea. The basic idea behind this windowing algorithm is very clear. In windowing technique each sample update the window rather than updating by classifier. A double window strategy shows in “Ref. [12]”.
j. Weighted Windows
Weighted window uses decay function. This decay function will helpful for flushing out previous samples which are of no need. Decay function allocates some weight to samples in window. In weighted window previous samples gains little weights and it has less privilege than new sample, that’s why they are less important than classifier. Cohen and Strauss provide use of decay functions for data stream aggregates calculation, given in “Ref. [13]”.
k. FISH
Zliobaite proposed a technique called FISH algorithm. FISH make a use of time and space uses time and space likenesses between samples for creating window dynamically. The standard hypothesis is given in “Ref. [14]”. In which the selection of samples is based on distance measure. To properly manage the balance in between time and distance, the distance and time should be normalized. The next part of FISH is FISH2, FISH3. Time/space proportion is takes as parameter in FISH2 algorithm. While in FISH3 for each incoming new instance FISH3 algorithm computes time/space proportion and size of window differently.
So the Family of FISH algorithm is very useful while classification when the dynamic windowing technique is necessary in drift detection.
l. ADWIN
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For solving drift detection problem it required learning algorithm and hoeffding bounds algorithm. When change occurs in data samples drift detection method works. During the classification process the accuracy of the algorithm is improved. Since drift detection and classification algorithm adapted while change occurs in data stream.
This Paper describe about concept drift and its types, it’s requirement and details of changes. There are various techniques DDM, EDDM, DDD, Drift detection through resampling, HDDM, DCDD along with that some windowing techniques, ensemble methods, and statistical method are made known in this paper. DDM is very suitable for sudden concept drift but it lack in detecting the slow change in data stream so the next technique EDDM will help to overcome this drawback. In determining the error generation diversity plays an important role.

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