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2015 | OriginalPaper | Buchkapitel

A Survey on Supervised Classification on Data Streams

verfasst von : Vincent Lemaire, Christophe Salperwyck, Alexis Bondu

Erschienen in: Business Intelligence

Verlag: Springer International Publishing

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Abstract

The last ten years were prolific in the statistical learning and data mining field and it is now easy to find learning algorithms which are fast and automatic. Historically a strong hypothesis was that all examples were available or can be loaded into memory so that learning algorithms can use them straight away. But recently new use cases generating lots of data came up as for example: monitoring of telecommunication network, user modeling in dynamic social network, web mining, etc. The volume of data increases rapidly and it is now necessary to use incremental learning algorithms on data streams. This article presents the main approaches of incremental supervised classification available in the literature. It aims to give basic knowledge to a reader novice in this subject.

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Vorheriges Kapitel From the Web of Documents to the Linked Data

Nächstes Kapitel Knowledge Reuse: Survey of Existing Techniques and Classification Approach

This bound is not well used in many algorithms of incremental trees as explain in [55] but with not a very big influence on the results.

Multi-armed bandits explore and exploit online set of decisions, while minimizing the cumulated regret between the chosen decisions and the optimal decision. Originally, multi-armed bandits have been used in pharmacology to choose the best drug while minimizing the number of tests. Today, they tend to replace A/B testing for web site optimization (Google analytics), they are used for ad-serving optimization. They are well designed when the true class to predict is not known: for instance, in some domains the learning algorithm receives only partial feedback upon its prediction, i.e. a single bit of right-or-wrong, rather than the true label.

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Titel: A Survey on Supervised Classification on Data Streams
verfasst von: Vincent Lemaire
Christophe Salperwyck
Alexis Bondu
Verlag: Springer International Publishing
Buch: Business Intelligence
Print ISBN: 978-3-319-17550-8

Electronic ISBN: 978-3-319-17551-5

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-17551-5_4

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