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Erschienen in:
A Machine Learning Perspective on Big Data Analysis Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

An Overview of Concept Drift Applications

verfasst von : Indrė Žliobaitė, Mykola Pechenizkiy, João Gama

Erschienen in: Big Data Analysis: New Algorithms for a New Society

Verlag: Springer International Publishing

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Abstract

In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks. First we overview and categorize application tasks for which the problem of concept drift is particularly relevant. Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.

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Vorheriges Kapitel Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement
Nächstes Kapitel Analysis of Text-Enriched Heterogeneous Information Networks
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Metadaten
Titel
An Overview of Concept Drift Applications
verfasst von
Indrė Žliobaitė
Mykola Pechenizkiy
João Gama
Copyright-Jahr
2016
Buch
Big Data Analysis: New Algorithms for a New Society

Print ISBN: 978-3-319-26987-0

Electronic ISBN: 978-3-319-26989-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-26989-4_4