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<span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">******Apologies for multiple posting******</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">
<br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">
<span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">_____________________________________________</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">
<span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"></span><span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"></span> <font color="#000000" class="">EAIS
2022</font><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">
<span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"></span><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Special Session</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">
<span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"></span><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> on</span>
<div class=""><font color="#000000" class="">Active Learning for Concept and Feature Drift Detection</font></div>
<div class=""><font color="#000000" class=""><a href="https://sites.google.com/view/cisslabssfuzzieee/home" class="">https://sites.google.com/view/cisslabssfuzzieee/home</a></font></div>
<div class=""><font color="#000000" class=""><a href="https://cyprusconferences.org/eais2022/" class="">https://cyprusconferences.org/eais2022/</a></font></div>
<div class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">_____________________________________________</span></div>
<div class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br class="">
</span></div>
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<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">In the Deep Learning (DL) hyperconnected era, classifiers consume labels and labelled data at unprecedented rates. While computational
power is no more an issue in model training, the time, cost, or human supervision required to produce high quality labelled data seriously hinders the maximum size of the available labelled datasets and hence the extensibility of DL in many challenging domains.
To tackle the label-scarcity problem, Transfer Learning (TL) and Active Learning (AL) have both been exploited, the former using pre-trained models from a different domain and the latter choosing the best subset of instances to be labelled. Specifically, in
the case of streaming data, labels may not be available for each instance of the stream (or being very costly, or come too fast for a human expert), data may have a very short lifespan, batch methods are unenforceable and issues due to concept and feature
drift, or model switch, may prevent the training to be effective. The aim of the special session is to host original papers and reviews on recent research advances and state‐of‐the‐art methods in the fields of Computational Intelligence, Machine Learning,
Data Mining and Distributed Computing methodologies concerning TL and AL techniques for concept and feature drift detection on streaming data.<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class=""> </span></div>
<div style="margin: 0cm; text-align: justify;" class=""><b class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Relevant topics within this context include, but are not limited to: <o:p class=""></o:p></span></b></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Computational Intelligence<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Machine learning and Deep Learning<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Sparse Coding<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Data Mining<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Fuzzy and Neuro‐Fuzzy Systems<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Probabilistic and statistical modelling<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Active Learning<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Transfer Learning<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Cost-Sensitive Learning<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Online Learning<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Concept Drift Detection<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Feature Drift Detection<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Online Feature Learning/Extraction/Selection<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class=""> </span></div>
<div style="margin: 0cm; text-align: justify;" class=""><b class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Important Dates:<o:p class=""></o:p></span></b></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Paper submission: January 10, 2022<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Notification of acceptance/rejection: February 19, 2022<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Camera ready submission: March 20, 2022<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class=""> </span></div>
<div style="margin: 0cm; text-align: justify;" class=""><b class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">Submission:<o:p class=""></o:p></span></b></div>
<div style="margin: 0cm; text-align: justify;" class=""><span style="font-family: Calibri, sans-serif;" class="">Submitted papers should not exceed 8 pages plus at most 2 pages overlength<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span style="font-family: Calibri, sans-serif;" class="">Submissions of full papers are accepted online through </span><span lang="EN-US" style="font-family: Calibri, sans-serif;" class="">EasyChair system<o:p class=""></o:p></span></div>
<div style="margin: 0cm; text-align: justify;" class=""><span lang="EN-US" style="font-family: Calibri, sans-serif;" class=""><a href="https://cyprusconferences.org/eais2022/index.php/submission/" class="">https://cyprusconferences.org/eais2022/index.php/submission/</a></span></div>
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