ECML-PKDD 2016 Network Classification Challenge

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Dear Colleagues,

In addition to our three exciting challenges, we have prepared a very
innovative ECML-PKDD 2016 discovery challenge, which collocates in the
realm of Automatic Network Management.

This challenge is one of the first explorations of ML for automatic network
analysis. Our goal is to promote the use of ML for network-related tasks in
general and, at the same time, to assess the participants' ability to
quickly build a learning-based system showing a reliable performance.

Please see more information at

http://www.neteye-blog.com/netcla-the-ecml-pkdd-network-classification-challenge/

Schedule

Aug.  12: the challenge starts, registration opens

Sept.  7: test data released, submission page opens

Sept. 10: submissions due

Sept. 12: Results and Paper invitations

Sept. 23: ECML-PKDD challenge track


The schedule is tight but we have encoded the network data using simple
feature vectors for learning a multi-class, single label, classification
task. Thus, you can simply try your own multiclass classification
algorithms and watch if they improve on strong baselines.

The aim is to find out which ML algorithms can better deal with this kind
of data.

Best,

Alessandro and Elio

Discovery Challenge Chairs of ECML-PKDD 2016

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<div dir=3D"ltr"><span id=3D"docs-internal-guid-295f508e-8d1d-0601-06cc-9c0=
7a23e0bbc"><p dir=3D"ltr" style=3D"line-height:1.38;margin-top:0pt;margin-b=
ottom:0pt"><span style=3D"font-size:12.6667px;font-family:Arial;vertical-al=
ign:baseline;white-space:pre-wrap">Dear Colleagues,</span></p><br><p dir=3D=
"ltr" style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span sty=
le=3D"font-size:12.6667px;font-family:Arial;vertical-align:baseline;white-s=
pace:pre-wrap">In addition to our three exciting challenges, we have prepar=
ed a very innovative ECML-PKDD 2016 discovery challenge, which collocates i=
n the realm of Automatic Network Management. </span></p><br><p dir=3D"ltr" =
style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"=
font-size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:p=
re-wrap">This challenge is one of the first explorations of ML for automati=
c network analysis. Our goal is to promote the use of ML for network-relate=
d tasks in general and, at the same time, to assess the participants&#39; a=
bility to quickly build a learning-based system showing a reliable performa=
nce. =C2=A0</span></p><br><p dir=3D"ltr" style=3D"line-height:1.38;margin-t=
op:0pt;margin-bottom:0pt"><span style=3D"font-size:12.6667px;font-family:Ar=
ial;vertical-align:baseline;white-space:pre-wrap">Please see more informati=
on at</span></p><br><p dir=3D"ltr" style=3D"line-height:1.38;margin-top:0pt=
;margin-bottom:0pt"><a href=3D"http://www.neteye-blog.com/netcla-the-ecml-p=
kdd-network-classification-challenge/" style=3D"text-decoration:none"><span=
 style=3D"font-size:12.6667px;font-family:Arial;text-decoration:underline;v=
ertical-align:baseline;white-space:pre-wrap">http://www.neteye-blog.com/net=
cla-the-ecml-pkdd-network-classification-challenge/</span></a></p><br><p di=
r=3D"ltr" style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span=
 style=3D"font-size:12.6667px;font-family:Arial;vertical-align:baseline;whi=
te-space:pre-wrap">Schedule</span></p><p dir=3D"ltr" style=3D"line-height:1=
.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"font-size:12.6667px;fo=
nt-family:Arial;vertical-align:baseline;white-space:pre-wrap">Aug. =C2=A012=
: the challenge starts, registration opens</span></p><p dir=3D"ltr" style=
=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"font-=
size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:pre-wr=
ap">Sept. =C2=A07: test data released, submission page opens</span></p><p d=
ir=3D"ltr" style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><spa=
n style=3D"font-size:12.6667px;font-family:Arial;vertical-align:baseline;wh=
ite-space:pre-wrap">Sept. 10: submissions due</span></p><p dir=3D"ltr" styl=
e=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"font=
-size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:pre-w=
rap">Sept. 12: Results and Paper invitations</span></p><p dir=3D"ltr" style=
=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"font-=
size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:pre-wr=
ap">Sept. 23: ECML-PKDD challenge track</span></p><br><br><p dir=3D"ltr" st=
yle=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"fo=
nt-size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:pre=
-wrap">The schedule is tight but we have encoded the network data using sim=
ple feature vectors for learning a multi-class, single label, classificatio=
n task. Thus, you can simply try your own multiclass classification algorit=
hms and watch if they improve on strong baselines.</span></p><br><p dir=3D"=
ltr" style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span styl=
e=3D"font-size:12.6667px;font-family:Arial;vertical-align:baseline;white-sp=
ace:pre-wrap">The aim is to find out which ML algorithms can better deal wi=
th this kind of data.</span></p><br><p dir=3D"ltr" style=3D"line-height:1.3=
8;margin-top:0pt;margin-bottom:0pt"><span style=3D"font-size:12.6667px;font=
-family:Arial;vertical-align:baseline;white-space:pre-wrap">Best,</span></p=
><p dir=3D"ltr" style=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"=
><span style=3D"font-size:12.6667px;font-family:Arial;vertical-align:baseli=
ne;white-space:pre-wrap">Alessandro and Elio</span></p><p dir=3D"ltr" style=
=3D"line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style=3D"font-=
size:12.6667px;font-family:Arial;vertical-align:baseline;white-space:pre-wr=
ap">Discovery Challenge Chairs of ECML-PKDD 2016 </span></p><br></span></di=
v>

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