Data Processing

A. Bifet's Adaptive Stream Mining: Pattern Learning and Mining from PDF

Posted On February 23, 2018 at 5:13 pm by / Comments Off on A. Bifet's Adaptive Stream Mining: Pattern Learning and Mining from PDF

By A. Bifet

ISBN-10: 1607500906

ISBN-13: 9781607500902

This publication is an important contribution to the topic of mining time-changing facts streams and addresses the layout of studying algorithms for this goal. It introduces new contributions on a number of diversified facets of the matter, choosing study possibilities and lengthening the scope for functions. it's also an in-depth learn of flow 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 the grounds that this has rigorous functionality promises, utilizing it instead of counters or accumulators, it bargains the potential for extending such promises to studying and mining algorithms no longer first and foremost designed for drifting facts. trying out with a number of tools, together with Na??ve Bayes, clustering, selection bushes and ensemble tools, is mentioned besides. the second one a part of the ebook describes a proper learn of attached acyclic graphs, or bushes, from the perspective of closure-based mining, providing effective algorithms for subtree checking out and for mining ordered and unordered common closed timber. finally, a normal technique to spot closed styles in an information circulate is printed. this can be utilized to enhance an incremental approach, a sliding-window dependent strategy, and a style that mines closed bushes adaptively from info streams. those are used to introduce type equipment for tree info streams.IOS Press is a world technological know-how, technical and clinical writer of high quality books for lecturers, scientists, and execs in all fields. a few of the components we submit in: -Biomedicine -Oncology -Artificial intelligence -Databases and knowledge structures -Maritime engineering -Nanotechnology -Geoengineering -All elements of physics -E-governance -E-commerce -The wisdom economic system -Urban reports -Arms keep an eye on -Understanding and responding to terrorism -Medical informatics -Computer Sciences

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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.

GMCR04] compares the window that contains all the data with a subwindow of data from the beginning until it detects that the accuracy of the algorithm decreases. The strategy of ADWIN, the method presented in next chapter, will be to compare all the adjacent subwindows in which is possible to partition the 44 CHAPTER 3. MINING EVOLVING DATA STREAMS window containing all the data. 6 shows these window management strategies. 3 a general framework for time change detectors and predictors. 5. 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.

The sufficient statistics of the leaf are initialized with the examples in the short term memory that maintains a limited number of the most recent examples. It is possible to observe an increase of the error reaching the warning level, followed by a decrease. This method uses the information already available to the learning algorithm and does not require additional computational resources. An advantage of this method is it continuously monitors the online error of Na¨ıve Bayes. It can detect changes in the class-distribution of the examples at any time.

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


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