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Get Adaptive Stream Mining: Pattern Learning and Mining from PDF

By A. Bifet

ISBN-10: 1607500906

ISBN-13: 9781607500902

This ebook is an important contribution to the topic of mining time-changing information streams and addresses the layout of studying algorithms for this function. It introduces new contributions on numerous diversified elements of the matter, choosing learn possibilities and extending the scope for functions. additionally it is an in-depth research of movement mining and a theoretical research of proposed tools and algorithms. the 1st part is anxious with using an adaptive sliding window set of rules (ADWIN). on account that this has rigorous functionality promises, utilizing it rather than counters or accumulators, it deals the potential for extending such promises to studying and mining algorithms now not firstly designed for drifting information. trying out with numerous equipment, together with Naïve Bayes, clustering, determination bushes and ensemble equipment, is mentioned besides. the second one a part of the publication describes a proper learn of attached acyclic graphs, or bushes, from the viewpoint of closure-based mining, offering effective algorithms for subtree checking out and for mining ordered and unordered common closed timber. finally, a normal method to spot closed styles in an information movement is printed. this can be utilized to strengthen an incremental approach, a sliding-window established approach, and a style that mines closed bushes adaptively from information streams. those are used to introduce type tools for tree info streams.

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Additional info for Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams

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Estimates are a particularly efficient method for estimating the performance of an SVM, estimating the leave-one-out-error of a SVM based solely on the one SVM solution learned with all examples. Each example z = (x, y) consists of a feature vector x ∈ RN and a label y ∈ {−1, +1} indicating its classification. Data arrives over time in batches of equal size, each containing m examples. For each batch i the data is independently identically distributed with respect to a distribution Pri(x, y). The goal of the learner L is to sequentially predict the labels of the next batch.

However, we could have used this test on the methods of Chapter 4. The Kolmogorov-Smirnov test [Kan06] is another statistical test used to compare two populations. Given samples from two populations, the cumulative distribution functions can be determined and plotted. Hence the maximum value of the difference between the plots can be found and compared with a critical value. If the observed value exceeds the critical value, H0 is rejected and a change is detected. It is not obvious how to implement the Kolmogorov-Smirnov test dealing with data streams.

CVFDT keeps the model it is learning in sync with such changing concepts by continuously monitoring the quality of old search decisions with respect to a sliding window of data from the data stream, and updating them in a fine-grained way when it detects that the distribution of data is changing. In particular, it maintains sufficient statistics throughout time for every candidate M considered at every search step. After the first w examples, where w is the window width, it subtracts the oldest example from these statistics whenever a new one is added.

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Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams by A. Bifet


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