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Erschienen in:
Signal Processing Methods for Light Field Displays Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Finding It Now: Networked Classifiers in Real-Time Stream Mining Systems

verfasst von : Raphael Ducasse, Cem Tekin, Mihaela van der Schaar

Erschienen in: Handbook of Signal Processing Systems

Verlag: Springer International Publishing

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Abstract

The aim of this chapter is to describe and optimize the specifications of signal processing systems, aimed at extracting in real time valuable information out of large-scale decentralized datasets. A first section will explain the motivations and stakes and describe key characteristics and challenges of stream mining applications. We then formalize an analytical framework which will be used to describe and optimize distributed stream mining knowledge extraction from large scale streams. In stream mining applications, classifiers are organized into a connected topology mapped onto a distributed infrastructure. We will study linear chains and optimise the ordering of the classifiers to increase accuracy of classification and minimise delay. We then present a decentralized decision framework for joint topology construction and local classifier configuration. In many cases, accuracy of classifiers are not known beforehand. In the last section, we look at how to learn online the classifiers characteristics without increasing computation overhead. Stream mining is an active field of research, at the crossing of various disciplines, including multimedia signal processing, distributed systems, machine learning etc. As such, we will indicate several areas for future research and development.

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Vorheriges Kapitel Inertial Sensors and Their Applications
Nächstes Kapitel Deep Neural Networks: A Signal Processing Perspective
Fußnoten
1
As we will discuss later, there are two types of configuration choices we must make: the topological ordering of classifiers and the local operating points at each classifier.
 
2
For example, since the operating point p F = 0 corresponds to a saddle point of the utility function, it would achieve steepest utility slope. Furthermore, the slope of the ROC curve is maximal at p F = 0 (due to concavity of the ROC curve), such that high detection probabilities can be obtained under low false alarm probabilities near the origin.
 
3
Furthermore, when classifiers are independent, the transition matrices \({T}^{{\upsigma }}_{i}\) are diagonal and therefore commute. As a consequence the end throughput t N(x) and goodput g N(x) are independent of the order. However, intermediate throughputs do depend on the ordering—leading to varying expected delays for the overall processing.
 
4
Observe that for a perfect classifier (\(p_{{\upsigma }(h)}^D=1\) and \(p_{{\upsigma }(h)}^F=0\)), the a-priori conditional probability \({\upphi }^{\upsigma }_h\) and the ex-post conditional probabilities \({\uppsi }^{\upsigma }_h\) are equal.
 
5
t i−1 and g i−1 are not required since: \(\mathrm {argmax}\ U_i = \mathrm {argmax}\ \frac {U_i}{g_{i-1}} = \left (-\left [\begin {array}{cccccccccccccccccccccccccc}{\uprho }_i & 0\end {array}\right ] + \left [\begin {array}{cccccccccccccccccccccccccc}v_{i+1} & w_{i+1}\end {array}\right ]{T}^{{\upsigma }}_{i}\right )\left [\begin {array}{cccccccccccccccccccccccccc}{\uptheta }_i\\1\end {array}\right ]\).
 
6
The utility parameters \(\left [\begin {array}{cccccccccccccccccccccccccc}v_j & w_j\end {array}\right ]\) fed back from classifier C j to classifier C i are independent of any classifiers’ operating points.
 
7
The definition of irrelevant context types can be relaxed to include all context types which have an effect that is less than 𝜖 > 0 on the classifier accuracies.
 
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Metadaten
Titel
Finding It Now: Networked Classifiers in Real-Time Stream Mining Systems
verfasst von
Raphael Ducasse
Cem Tekin
Mihaela van der Schaar
Copyright-Jahr
2019
Buch
Handbook of Signal Processing Systems

Print ISBN: 978-3-319-91733-7

Electronic ISBN: 978-3-319-91734-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-91734-4_3

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