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35
KDD Cup 2009 Fast Scoring on a Large Database Presentation of the Results at the KDD Cup Workshop June 28, 2008 The Organizing Team
Transcript of all categories internal usage - ClopiNet€¦ · PPT file · Web view · 2009-06-26Sampath...
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KDD Cup2009
Fast Scoring on a Large Database
Presentation of the Results at the KDD Cup Workshop
June 28, 2008
The Organizing Team
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KDD Cup 2009 Organizing Team
Project team at Orange Labs R&D:
Vincent Lemaire Marc Boull Fabrice Clrot Raphal Fraud Aurlie Le Cam Pascal Gouzien
Beta testing and proceedings editor:
Gideon Dror
Web site design:
Olivier Guyon (MisterP.net, France)
Coordination (KDD cup co-chairs):
Isabelle Guyon David Vogel
The main objective of the challenge is to make good predictions of the target variables. The prediction of each target variable is thought of as a separate classification problem. The results of classification, obtained by thresholding the prediction score, may be represented in a confusion matrix, where tp (true positive), fn (false negative), tn (true negative) and fp (false positive) represent the number of examples falling into each possible outcome:
PredictionClass +1Class -1TruthClass +1tpfnClass -1fptn
We define the sensitivity (also called true positive rate or hit rate) and the specificity (true negative rate) as:Sensitivity = tp/posSpecificity = tn/negwhere pos=tp+fn is the total number of positive examples and neg=tn+fp the total number of negative examples.
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Thanks to our sponsors
OrangeACM SIGKDDPascalUnipenGoogleHealth Discovery CorpClopinetData Mining SolutionsMPS
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Record KDD Cup Participation
Year# Teams1997451998571999242000312001136200218200357200410220053720066820079520081282009453
Chart1
45
57
24
31
136
18
57
102
37
68
95
128
453
# Teams
Year
KDD Cup Participation By Year
Sheet4
Domain ExtensionCountry
DomainTotalArgentina3a(unknown)
Count of Domain.arar3Argentina3Australia5bitnet(unknown)
DomainTotal.atat3Austria3Austria3acUnited Kingdom academic institutions
.ar3.auau11Australia11Belgium11adAndorra
.at3.bebe11Belgium11Brazil1aeUnited Arab Emirates
.au11.bgbg1Bulgaria1Bulgaria1afAfghanistan
.be11.bizbiz202Canada12agAntigua and Barbuda
.bg1.brbr1Brazil1Chile1aiAnguilla
.biz2.c.zac.za101China25alAlbania
.br1.caca12Canada12Fiji25amArmenia
.c.za1.chch4Switzerland4Finland25anNetherlands Antilles
.ca12.clcl1Chile1France25aoAngola
.ch4.cncn25China25Germany37aqAntarctica
.cl1.comcom780US Commercial780Greece2arArgentina
.cn25.com1com1101Hong Kong20asAmerican Samoa
.com780.com2com2101Hungary14atAustria
.com11.coomcoom101India14auAustralia
.com21.dede37Germany37Iran14awAruba
.coom1.eduedu72US Educational72Ireland2azAzerbaijan
.de37.eses12Spain12Israel1baBosnia and Herzegovina
.edu72.eueu101Italy6bbBarbados
.es12.fifi3Finland3Japan6bdBangladesh
.eu1.fjfj1Fiji1Jordan2beBelgium
.fi3.frfr31France31Latvia1bfBurkina Faso
.fj1.grgr2Greece2Malaysia1bgBulgaria
.fr31.hkhk20Hong Kong20Mexico1bhBahrain
.gr2.huhu14Hungary14Netherlands1biBurundi
.hk20.ieie2Ireland2New Zealand3bjBenin
.hu14.ilil3Israel3Pakistan1bmBermuda
.ie2.inin11India11Portugal1bnBrunei Darussalam
.il3.irir1Iran1Romania1boBolivia
.in11.itit6Italy6Russian Federation25brBrazil
.ir1.jojo2Jordan2Singapore10bsBahamas
.it6.jpjp21Japan21Slovak Republic1btBhutan
.jo2.krkr8Korea (South)8Slovenia1bvBouvet Island
.jp21.lvlv1Latvia1South Africa1bwBotswana
.kr8.mxmx1Mexico1South Korea1byBelarus
.lv1.mymy1Malaysia1Spain1bzBelize
.mx1.netnet17US network17Sweden1caCanada
.my1.nlnl6Netherlands6Switzerland1ccCocos (Keeling) Islands
.net17.nznz2New Zealand (Aotearoa)2Taiwan52cfCentral African Republic
.nl6.oeoe101Turkey52cgCongo
.nz2.orgorg14US Non-Profit Organization14Uganda1chSwitzerland
.oe1.pkpk1Pakistan1United Kingdom19ciCote d'Ivoire (Ivory Coast)
.org14.plpl21021Uruguay1ckCook Islands
.pk1.ptpt5Portugal5United States887clChile
.pl21.roro1Romania1cmCameroon
.pt5.rsrs202cnChina
.ro1.ruru25Russian Federation25coColombia
.rs2.sese1Sweden1comUS Commercial
.ru25.sgsg10Singapore10crCosta Rica
.se1.sisi4Slovenia4csCzechoslovakia (former)
.sg10.sksk1Slovak Republic1cuCuba
.si4.trtr2Turkey2cvCape Verde
.sk1.twtw52Taiwan52cxChristmas Island
.tr2.ugug1Uganda1cyCyprus
.tw52.ukuk19United Kingdom19czCzech Republic
.ug1.uyuy1Uruguay1deGermany
.uk19.vivi1Virgin Islands (US)1djDjibouti
.uy1.xxxx101dkDenmark
.vi1.yyyy101dmDominica
.xx1.zaza2South Africa2doDominican Republic
.yy1Grand Totalrand Total1299dzAlgeria
.za2ecEcuador
Grand Total1299eduUS Educational
eeEstonia
egEgypt
ehWestern Sahara
erEritrea
esSpain
etEthiopia
fiFinland
fjFiji
fkFalkland Islands (Malvinas)
fmMicronesia
foFaroe Islands
frFrance
fxFrance (Metropolitan)
gaGabon
gbGreat Britain (UK)
gdGrenada
geGeorgia
gfFrench Guiana
ghGhana
giGibraltar
glGreenland
gmGambia
gnGuinea
govUS Government
gpGuadaloupe
gqEquatorial Guinea
grGreece
gsSouth Georgia and South Sandwich Islands
gtGuatemala
guGuam
gwGuinea-Bissau
gyGuyana
hkHong Kong
hmHeard and McDonald Islands
hnHonduras
hrCroatia (Hrvatska)
htHaiti
huHungary
idIndonesia
ieIreland
ilIsrael
inIndia
ioBritish Indian Ocean Territory
iqIraq
irIran
isIceland
itItaly
jmJamaica
joJordan
jpJapan
keKenya
kgKyrgyzstan
khCambodia
kiKiribati
kmComoros
knSaint Kitts and Nevis
kpKorea (North)
krKorea (South)
kuKuwait
kyCayman Islands
kzKazakhstan
laLaos
lbLebanon
lcSaint Lucia
liLiechtenstein
lkSri Lanka
lrLiberia
lsLesotho
ltLithuania
luLuxembourg
lvLatvia
lyLibya
maMorocco
mcMonaco
mdMoldova
mgMadagascar
mhMarshall Islands
milUS Military
mkMacedonia
mlMali
mmMynamar
mnMongolia
moMacau
mpNorthern Mariana Islands
mqMartinique
mrMauritania
msMontserrat
mtMalta
muMauritius
mvMaldives
mwMalawi
mxMexico
myMalaysia
mzMozambique
naNamibia
ncNew Caledonia
neNiger
netUS network
nfNorfolk Island
ngNigeria
niNicaragua
nlNetherlands
noNorway
npNepal
nrNauru
ntNeutral Zone
nuNiue
nzNew Zealand (Aotearoa)
omOman
orgUS Non-Profit Organization
paPanama
pePeru
pfFrench Polynesia
pgPapua New Guinea
phPhilippines
pkPakistan
pl>Poland
pmSaint Pierre and Miquelon
pnPitcairn
prPuerto Rico
ptPortugal
pwPalau
pyParaguay
qaQatar
reReunion
roRomania
ruRussian Federation
rwRwanda
saSaudi Arabia
sbSolomon Islands
scSeychelles
sdSudan
seSweden
sgSingapore
shSaint Helena
siSlovenia
sjSvalbard and Jan Mayen Islands
skSlovak Republic
slSierra Leone
smSan Marino
snSenegal
soSomalia
srSuriname
stSao Tome and Principe
suUSSR (former)
svEl Salvador
sySyria
szSwaziland
tcTurks and Caicos Islands
tdChad
tfFrench Southern Territories
tgTogo
thThailand
tjTajikistan
tkTokelau
tmTurkmenistan
tnTunisia
toTonga
tpEast Timor
trTurkey
ttTrinidad and Tobago
tvTuvalu
twTaiwan
tzTanzania
uaUkraine
ugUganda
ukUnited Kingdom
umUS Minor Outlying Islands
usUnited States
uyUruguay
uzUzbekistan
vaVatican City State
vcSaint Vincent and the Grenadines
veVenezuela
vgVirgin Islands (British)
viVirgin Islands (US)
vnViet Nam
vuVanuatu
wfWallis and Futuna Islands
wsSamoa
yeYemen
ytMayotte
yuYugoslavia
zaSouth Africa
zmZambia
zrZaire
zwZimbabwe
Bulgaria
Bhutan
Belize
Denmark
Falkland Islands (Malvinas)
Georgia
Cambodia
Cayman Islands
French Polynesia
Slovenia
Uganda
Uzbekistan
Zambia
Sheet1
Domain
Pablo Mouzo 4138.ar
Marcelo Ferreyra 4643.ar
Andrs Parra 4138.ar
Michael Jahrer 4340.at
Markus Muhr 3431.at
Michael Pfeiffer 4138.at
Vladimir Nikulin 4340.au
Hugh Miller 4037.au
Gad Abraham 4239.au
Stephen Mc Ateer 4643.au
Tung Pham 3431.au
Luo Wei 2825.au
Tim Manns 3431.au
Mai Dang 3229.au
Jojo the Monkey 3431.au
vateesh chand 3734.au
Mai Dang 3936.au
Kevin Franoisse 4946.be
Pierre Geurts 3431.be
Peter Clerinx 4643.be
Kris De Brabanter 5350.be
Peter Karsmakers 5249.be
saaid hasbi 3633.be
cedric simon 4037.be
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Kristof Coussement 5249.be
koen de bock 3532.be
vencisalv trifonov 3633.bg
Phil Brierley 3430.biz
Sharon Lewis 3430.biz
Robson Cordeiro 3633.br
Musa Gabere 3025.c.za
Gordon Cormack 3835.ca
Abdellali Kelil 4845.ca
zhengzheng xing 3330.ca
Radford Neal 3936.ca
Alexei Nordell 4037.ca
Ruiwen Chen 3330.ca
Da Kuang 2421.ca
Tengke Xiong 4239.ca
Salar Sayyad 3936.ca
Lyon Chang 3128.ca
Vlado Keselj 3027.ca
Marcin Mizianty 3835.ca
Adam Woznica 3633.ch
Ali Ajdari Rad 3835.ch
Christian Sommer 3027.ch
Thomas Fuchs 3936.ch
Cristian Bravo 3936.cl
Ray Sun 2522.cn
Luheng He 3734.cn
bin wu 2623.cn
Minlong Lin 3936.cn
Wang Pu 3532.cn
Xiaoxing Yang 4037.cn
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Fang Wang 3532.cn
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Dong Wang 4138.cn
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Xi Chen 3128.cn
Han Zhi 2724.cn
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feng wang 3733.com
Cathy Wat 3127.com
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Shuangyong Song 3531.com
4743.com
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B T 2925.com
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I F 2319.com
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tus1337 tus1337 4036.com
Mathias Adankon 4339.com
Jean-Philippe Vert 3733.com
baptiste pigaux 3632.Com
Roger Zhan 2925.COM
Kyong Shim 3127.com
Jian Luo 2622.com
saeed dehghan 3531.com
Yuyin Sun 3834.com
Guang-Tong Zhou 4137.com
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Hao Wu 2622.com
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Bin Cao 2723.com
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Claudia Perlich 3632.com
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Vikas Raykar 3632.com
Chuck Lam 3430.com
DFT Boy 3127.com
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Ya Hu 2824.com
na na 2016.com
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xia wei 2521.com
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fabon dzogang 3935.com
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Ravi Sikhakolanu 4642.com
Yongming Shen 3531.com
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ASHISH TENDULKAR 3733.com
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anil rohilla 3632.com
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Frdric Clerc 3430.com
Jianming Jin 3531.com
Moninder Singh 3632.com
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Yu GONG 2723.com
Lian Liu 3430.com
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hsu sheng 3228.com
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long top 3228.com
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Ye Wang 3228.com
Srivani Narra 3329.com
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Henree Domen 3632.com
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Hao Liu 3228.com
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Xu Xu 3329.com
Mohammad Aziz 3531.com
arif akbulut 3228.com
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jiang jie 3026.com
X X 2319.com
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Zhibo Xiao 3228.com
Omar Cardona 3632.com
Bill Chen 3329.com
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XUEYAO LIANG 4339.com
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tae hyung Kim 3430.com
hyunkuyn yoo 3228.com
Heesu park 3329.com
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Hwang Heejin 3733.com
xu yanzi 2521.com
Wei Fan 2723.com
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Tiantian Xu 3531.com
Swetha Krish 3632.com
Xiangjun Zhou 3733.com
Martin Hong 3935.com
Heine Bodekaer 4036.com
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HUANG TERRY 3228.com
Suraj MS 3127.com
wt zh 2521.com
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Shih-Wei Tu 3026.com
Kenn Slagter 3834.com
Chia-Yu Tsai 3632.com
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John Trenkle 4541.com
Sheng-Kai Wang 3430.com
aravind gowrisankar 4339.com
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up chen 2622.com
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Anung Ariwibowo 3632.com
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Arun Kumar Mani 4339.com
Bo Han 2622.com
Claudio Favre 3632.com
Yuanming Yu 3026.com
PU Jiansu 3430.com
Lacr Wang 2925.com
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Marcin Piechota 4339.com
raghav ghaiee 3834.com
Marcos Souza 4238.com
Giuseppe Paleologo 3733.com
Nathaniel Ramm 4137.com
Brendan O\'Connor 3834.com
Willy Duville 4036.com
Li-Chin Lee 3430.com
heart Wang 3127.com
Evgenija Odinokova 3834.com
Ajay Ohri 3733.com
Botao Hu 2824.com
Xiatian Zhang 3935.com
Ihor Menshykov 3531.com
William Lao 3228.com
M Hofmann 3733.com
Alexandru Niculescu-Mizil 4642.com
Raghava Kothapalli 4137.com
Bahman Sodeir Abedi 3935.com
Dubravko Culibrk 4541.com
Marek Slonski 3935.com
Chih-heng Chung 4036.com
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Aisha Zaidan 3531.com
Wayne Thompson 3935.com
omar darwish 3632.com
Kurt Schwenke 3329.com
Ritendra Datta 4137.com
Mohammad Al-Masri 3632.com
ujwala kotti 3329.com
Alex Lin 3127.com
Ex Maple 2824.com
Mauricio Mercado 4137.com
Balazs Kegl 3531.com
Stefka Dabijeva 3632.com
Hsiao-Shan Hsieh 4137.com
ma shengxiang 3834.com
cloud computing 4945.com
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Elias Blomqvist 4339.com
Sachin Gangaputra 3834.com
Test Account 3531.com
Li Sheng 3329.com
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SARAFIDIS MICHAEL 4339.com
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Dryomov Dmitry 3430.com
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Dong Wang 2925.com
qdsunstar qdsunstar 3935.com
Shixi Chen 3228.com
Putri Novianti 3430.com
Ryan Skar 2925.com
Haoru Chen 3026.com
Sarika T 3834.com
Mohammad Ali Ghaderi 4339.com
chun-yao Huang 3935.com
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Xiaoyuan Hu 3430.com
ekker ko 2723.com
chunghung lin 3935.com
Yogesh Badhe 3127.com
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Song Xiang-Yu 4239.tw
Yi-Shan Lin 3734.tw
Chih-Ta Lin 3229.tw
Shou-Wei Ho 4037.tw
Miles Chiang 3734.tw
Cheng-Ju Kuo 4138.tw
YiHung Huang 3835.tw
Yu-Shi Lin 3936.tw
Bill Ko 3229.tw
Hungyi Lo 3633.tw
Y.S Lin 3027.tw
Hung-Yi Lo 3330.tw
A Lin 3431.tw
Li-Jen Chien 3128.tw
Hungyi Henry Lo 3734.tw
dog oauk 3431.tw
ekker ko 3128.tw
Eric Chen 3734.tw
ee rrrr 2623.tw
Shang-Tse Chen 3734.tw
Jan Nien-yi 3027.tw
Chao Chao Yen 4138.tw
Yu HsingChuan 3633.tw
Chih-ta Lin 4037.tw
Li-Jen Chien 3330.tw
Ernest Mwebaze 3936.ug
Tom Diethe 3431.uk
Gavin Cawley 3229.uk
Bassam Farran 3734.uk
Jurgen Van Gael 3330.uk
Markovsky Ivan 3532.uk
Andrzej Ruta 4037.uk
Paul Coleman 3734.uk
l han 2320.uk
Ben Galvin 3734.uk
Pam Green 3330.uk
Daniel Berrar 3835.uk
Leto Peel 3936.uk
Geoffrey Guile 3431.uk
Ashish Ganda 4037.uk
Enrico Glaab 3229.uk
Padmasini rengabashyam 4845.uk
Fedor Zhdanov 3532.uk
Ching-Wei Wang 3734.uk
Ian Hurst 3330.uk
Esther Hochsztain 3835.uy
Durban Frazer 3229.vi
lol lol 2421.xx
test test 2522.yy
Adriaan Pottas 4138.za
Dr. Sundararajan Vijayaraghava Seshadri 6057.za
Sheet2
Domain ExtensionCountry
a(unknown)
bitnet(unknown)
acUnited Kingdom academic institutions
adAndorra
aeUnited Arab Emirates
afAfghanistan
agAntigua and Barbuda
aiAnguilla
alAlbania
amArmenia
anNetherlands Antilles
aoAngola
aqAntarctica
arArgentina
asAmerican Samoa
atAustria
auAustralia
awAruba
azAzerbaijan
baBosnia and Herzegovina
bbBarbados
bdBangladesh
beBelgium
bfBurkina Faso
bgBulgaria
bhBahrain
biBurundi
bjBenin
bmBermuda
bnBrunei Darussalam
boBolivia
brBrazil
bsBahamas
btBhutan
bvBouvet Island
bwBotswana
byBelarus
bzBelize
caCanada
ccCocos (Keeling) Islands
cfCentral African Republic
cgCongo
chSwitzerland
ciCote d'Ivoire (Ivory Coast)
ckCook Islands
clChile
cmCameroon
cnChina
coColombia
comUS Commercial
crCosta Rica
csCzechoslovakia (former)
cuCuba
cvCape Verde
cxChristmas Island
cyCyprus
czCzech Republic
deGermany
djDjibouti
dkDenmark
dmDominica
doDominican Republic
dzAlgeria
ecEcuador
eduUS Educational
eeEstonia
egEgypt
ehWestern Sahara
erEritrea
esSpain
etEthiopia
fiFinland
fjFiji
fkFalkland Islands (Malvinas)
fmMicronesia
foFaroe Islands
frFrance
fxFrance (Metropolitan)
gaGabon
gbGreat Britain (UK)
gdGrenada
geGeorgia
gfFrench Guiana
ghGhana
giGibraltar
glGreenland
gmGambia
gnGuinea
govUS Government
gpGuadaloupe
gqEquatorial Guinea
grGreece
gsSouth Georgia and South Sandwich Islands
gtGuatemala
guGuam
gwGuinea-Bissau
gyGuyana
hkHong Kong
hmHeard and McDonald Islands
hnHonduras
hrCroatia (Hrvatska)
htHaiti
huHungary
idIndonesia
ieIreland
ilIsrael
inIndia
ioBritish Indian Ocean Territory
iqIraq
irIran
isIceland
itItaly
jmJamaica
joJordan
jpJapan
keKenya
kgKyrgyzstan
khCambodia
kiKiribati
kmComoros
knSaint Kitts and Nevis
kpKorea (North)
krKorea (South)
kuKuwait
kyCayman Islands
kzKazakhstan
laLaos
lbLebanon
lcSaint Lucia
liLiechtenstein
lkSri Lanka
lrLiberia
lsLesotho
ltLithuania
luLuxembourg
lvLatvia
lyLibya
maMorocco
mcMonaco
mdMoldova
mgMadagascar
mhMarshall Islands
milUS Military
mkMacedonia
mlMali
mmMynamar
mnMongolia
moMacau
mpNorthern Mariana Islands
mqMartinique
mrMauritania
msMontserrat
mtMalta
muMauritius
mvMaldives
mwMalawi
mxMexico
myMalaysia
mzMozambique
naNamibia
ncNew Caledonia
neNiger
netUS network
nfNorfolk Island
ngNigeria
niNicaragua
nlNetherlands
noNorway
npNepal
nrNauru
ntNeutral Zone
nuNiue
nzNew Zealand (Aotearoa)
omOman
orgUS Non-Profit Organization
paPanama
pePeru
pfFrench Polynesia
pgPapua New Guinea
phPhilippines
pkPakistan
pl>Poland
pmSaint Pierre and Miquelon
pnPitcairn
prPuerto Rico
ptPortugal
pwPalau
pyParaguay
qaQatar
reReunion
roRomania
ruRussian Federation
rwRwanda
saSaudi Arabia
sbSolomon Islands
scSeychelles
sdSudan
seSweden
sgSingapore
shSaint Helena
siSlovenia
sjSvalbard and Jan Mayen Islands
skSlovak Republic
slSierra Leone
smSan Marino
snSenegal
soSomalia
srSuriname
stSao Tome and Principe
suUSSR (former)
svEl Salvador
sySyria
szSwaziland
tcTurks and Caicos Islands
tdChad
tfFrench Southern Territories
tgTogo
thThailand
tjTajikistan
tkTokelau
tmTurkmenistan
tnTunisia
toTonga
tpEast Timor
trTurkey
ttTrinidad and Tobago
tvTuvalu
twTaiwan
tzTanzania
uaUkraine
ugUganda
ukUnited Kingdom
umUS Minor Outlying Islands
usUnited States
uyUruguay
uzUzbekistan
vaVatican City State
vcSaint Vincent and the Grenadines
veVenezuela
vgVirgin Islands (British)
viVirgin Islands (US)
vnViet Nam
vuVanuatu
wfWallis and Futuna Islands
wsSamoa
yeYemen
ytMayotte
yuYugoslavia
zaSouth Africa
zmZambia
zrZaire
zwZimbabwe
Bulgaria
Bhutan
Belize
Denmark
Falkland Islands (Malvinas)
Georgia
Cambodia
Cayman Islands
French Polynesia
Slovenia
Uganda
Uzbekistan
Zambia
Sheet3
ArgentinaGermanyMalaysiaSouth KoreaYear# TeamsregRecordsPredictors
AustraliaGreeceMexicoSpain199745
AustriaHong KongNetherlandsSweden199857
BelgiumHungaryNew ZealandSwitzerland199924
BrazilIndiaPakistanTaiwan200031170
BulgariaIranPortugalTurkey20011361909139351
CanadaIrelandRomaniaUganda200218
ChileIsraelRussian FederationUnited Kingdom200357
ChinaItalySingaporeUruguay2004102
FijiJapanSlovak RepublicUnited States200537
FinlandJordanSlovenia200668
FranceLatviaSouth Africa200795netflix
2008128
2009453
Sheet3
0
0
0
0
0
0
0
0
0
0
0
0
0
# Teams
Year
KDD Cup Participation By Year
-
Participation Statistics
1299 registered teams7865 entries46 countries :
ArgentinaGermanyMalaysiaSouth KoreaAustraliaGreeceMexicoSpainAustriaHong KongNetherlandsSwedenBelgiumHungaryNew ZealandSwitzerlandBrazilIndiaPakistanTaiwanBulgariaIranPortugalTurkeyCanadaIrelandRomaniaUgandaChileIsraelRussian FederationUnited KingdomChinaItalySingaporeUruguayFijiJapanSlovak RepublicUnited StatesFinlandJordanSloveniaFranceLatviaSouth Africa
-
A worlwide operator
One of the main telecommunication operators in the world
Providing services to more than 170 millions customers over five continents
Including 120 millions under the Orange Brand
-
KDD Cup 2009 organized by OrangeCustomer Relationship Management (CRM)
Three marketing tasks: predict the propensity of customers
to switch provider: Churn
to buy new products or services: Appentency
to buy upgrades or new options proposed to them: Up-selling
Objective: improve the return of investments (ROI) of marketing campaigns
Increase the efficiency of the campaign given a campaign cost
Decrease the campaign cost for a given marketing objective
Better prediction leads to better ROI
-
Data, constraints and requirements
Input data
Relational databases
Numerical or categorical
Noisy
Missing values
Heavily unbalanced distribution
Train data
Hundreds of thousands of instances
Tens of thousand of variables
Deployment
Tens of millions of instances
Train and deploy requirements
About one hundred models per month
Fast data preparation and modeling
Fast deployment
Model requirements
Robust
Accurate
Understandable
Business requirement
Return of investment for the whole process
-
In-house systemFrom raw data to scoring models
Data warehouse
Relational data base
Data mart
Star schema
Feature construction
PAC technology
Generates tens of thousands of variables
Data preparation and modeling
Khiops technology
Data feeding
PAC
Khiops
-
Design of the challenge
Orange business objective
Benchmark the in-house system against state of the art techniques
Data
Data store
Not an option
Data warehouse
Confidentiality and scalability issues
Relational data requires domain knowledge and specialized skills
Tabular format
Standard format for the data mining community
Domain knowledge incorporated using feature construction (PAC)
Easy anonymization
Tasks
Three representative marketing tasks
Requirements
Fast data preparation and modeling (fully automatic)
Accurate
Fast deployment
Robust
Understandable
-
Data sets extraction and preparation
Input data
10 relational table
A few hundreds of fields
One million customers
Instance selection
Resampling given the three marketing tasks
Keep 100 000 instances, with less unbalanced target distributions
Variable construction
Using PAC technology
20000 constructed variables to get a tabular representation
Keep 15 000 variables (discard constant variables)
Small track: subset of 230 variables related to classical domain knowledge
Anonymization
Discard variable names, discard identifiers
Randomize order of variables
Rescale each numerical variable by a random factor
Recode each categorical variable using random category names
Data samples
50 000 train and test instances sampled randomly
5000 validation instances sampled randomly from the test set
-
Scientific and technical challenge
Scientific objective
Fast data preparation and modeling: within five days
Large scale: 50 000 train and test data, 15 000 variables
Hetegeneous data
Numerical with missing values
Categorical with hundreds of values
Heavily unbalanced distribution
KDD social meeting objective
Attract as many participants as possible
Additional small track and slow track
Online feedback on validation dataset
Toy problem (only one informative input variable)
Leverage challenge protocol overhead
One month to explore descriptive data and test submission protocol
Attractive conditions
No intellectual property conditions
Money prizes
-
Business impact of the challenge
Bring Orange datasets to the data mining community
Benefit for community
Access to challenging data
Benefit for Orange
Benchmark of numerous competing techniques
Drive the research efforts towards Orange needs
Evaluate the Orange in-house system
High number of participants and high quality of the results
Orange in-house results:
Improved by a significant margin when leveraging all business requirements
Almost Parretto optimal when other criterions are considered
(automation, very fast train and deploy, robustness and understandability)
Need to study the best challenge methods to get more insights
-
KDD Cup 2009: Result Analysis
Best Result (period considered in the figure)
In House System (downloadable : www.khiops.com)
Baseline (Nave Bayes)
-
Overall Test AUC Fast
Good Result Very Quickly
Best Results (on each dataset) Submissions
-
Overall Test AUC Fast
Good Result Very Quickly
Best Results (on each dataset) Submissions
In House (Orange) System:
No parametersOn 1 standard laptop (mono proc)If deal as 3 different problems
-
Overall Test AUC Fast
Very Fast Good Result
Small improvement after the first day
(83.85 84.93)
-
Overall Test AUC Slow
Very Small improvement after the 5th day
(84.93 85.2)
Improvement due to unscrambling?
-
Overall Test AUC Submissions
23.24% of the submissions (>0.5)< Baseline
15.25% of the submissions (>0.5)> In House
84.75% of the submissions (>0.5)< In House
-
Overall Test AUC 'Correlation' Test / Valid
-
Overall Test AUC'Correlation' Test / Train
Random Values Submitted
Boosting Method orTrain Target Submitted
Over fitting
?
-
Overall Test AUC
Test AUC - 12 hours
Test AUC - 24 hours
Test AUC 36 days
Test AUC 5 days
-
Overall Test AUC
Test AUC - 12 hours
Test AUC 36 days
time to adjust model parameters ? time to train ensemble method ? time to find more processors ? time to test more methods time to unscramble ?
Difference between :
best result at the end of the first day and best result at the end of the 36 days
=1.35%
-
Test AUC = f (time)
Easier ?
Harder ?
Churn Test AUC day [0:36]
Appetency Test AUC day [0:36]
Up-selling Test AUC day [0:36]
-
Test AUC = f (time)
Easier ?
Harder ?
Difference between :
best result at the end of the first day and best result at the end of the 36 days
=1.84%
=1.38%
=0.11%
Churn Test AUC day [0:36]
Appetency Test AUC day [0:36]
Up-selling Test AUC day [0:36]
-
CorrelationTest AUC / Valid AUC (5 days)
Easier ?
Harder ?
Churn Test/Valid day [0:5]
Appetency Test/Valid day [0:5]
Up-selling Test/Valid day [0:5]
-
CorrelationTrain AUC / Valid AUC (36 days)
Difficulty to conclude something
Churn Test/Train day [0:36]
Appetency Test/Train day [0:36]
Up-selling Test/Train day [0:36]
-
HistogramTest AUC / Valid AUC ([0:5] or ]5-36] days)
Knowledge (parameters?) found during 5 days helps after ?
Churn Test AUC day [0:36]
Appetency Test AUC day [0:36]
Up-selling Test AUC day [0:36]
-
HistogramTest AUC / Valid AUC ([0:5] or ]5-36] days)
Knowledge (parameters?) found during 5 days helps after ?
YES !
Churn Test AUC day [0:36]
Appetency Test AUC day [0:36]
Up-selling Test AUC day [0:36]
Churn Test AUC day ]5:36]
Appetency Test AUC day ]5:36]
Up-selling Test AUC day ]5:36]
-
Fact Sheets:Preprocessing & Feature Selection
0
20
40
60
80
Principal Component Analysis
Other prepro
Grouping modalities
Normalizations
Discretization
Replacement of the missing values
PREPROCESSING (overall usage=95%)
Percent of participants
0
10
20
30
40
50
60
Wrapper with search
Embedded method
Other FS
Filter method
Feature ranking
FEATURE SELECTION (overall usage=85%)
Percent of participants
Forward / backward wrapper
-
Fact Sheets:Classifier
0
10
20
30
40
50
60
Bayesian Neural Network
Bayesian Network
Nearest neighbors
Nave Bayes
Neural Network
Other Classif
Non-linear kernel
Linear classifier
Decision tree...
CLASSIFIER (overall usage=93%)
Percent of participants
About 30% logistic loss, >15% exp loss, >15% sq loss, ~10% hinge loss. Less than 50% regularization (20% 2-norm, 10% 1-norm). Only 13% unlabeled data.
-
Fact Sheets: Model Selection
0
10
20
30
40
50
60
Bayesian
Bi-level
Penalty-based
Virtual leave-one-out
Other cross-valid
Other-MS
Bootstrap est
Out-of-bag est
K-fold or leave-one-out
10% test
MODEL SELECTION (overall usage=90%)
Percent of participants
About 75% ensemble methods (1/3 boosting, 1/3 bagging, 1/3 other). About 10% used unscrambling.
-
Fact Sheets: Implementation
Run in parallel
Multi-processor
None
Memory
Operating System
Parallelism
Software Platform
-
Winning methods
Fast track:
IBM research, USA +: Ensemble of a wide variety of classifiers. Effort put into coding (most frequent values coded with binary features, missing values replaced by mean, extra features constructed, etc.) ID Analytics, Inc., USA +: Filter+wrapper FS. TreeNet by Salford Systems an additive boosting decision tree technology, bagging also used. David Slate & Peter Frey, USA: Grouping of modalities/discretization, filter FS, ensemble of decision trees.
Slow track:
University of Melbourne: CV-based FS targeting AUC. Boosting with classification trees and shrinkage, using Bernoulli loss. Financial Engineering Group, Inc., Japan: Grouping of modalities, filter FS using AIC, gradient tree-classifier boosting. National Taiwan University +: Average 3 classifiers: (1) Solve joint multiclass problem with l1-regularized maximum entropy model. (2) AdaBoost with tree-based weak leaner. (3) Selective Nave Bayes.
(+: small dataset unscrambling)
-
Conclusion
Participation exceeded our expectations. We thank the participants for their hard work, our sponsors, and Orange who offered:
A problem of real industrial interest with challenging scientific and technical aspects
Prizes.
Lessons learned:
Do not under-estimate the participants: five days were given for the fast challenge, only a few hours sufficed to some participants.
Ensemble methods are effective.
Ensemble of decision trees offer off-the-shelf solutions to problems with large numbers of samples and attributes, mixed types of variables, and lots of missing values.
presentation title
The main objective of the challenge is to make good predictions of the target variables. The prediction of each target variable is thought of as a separate classification problem. The results of classification, obtained by thresholding the prediction score, may be represented in a confusion matrix, where tp (true positive), fn (false negative), tn (true negative) and fp (false positive) represent the number of examples falling into each possible outcome:
PredictionClass +1Class -1TruthClass +1tpfnClass -1fptn
We define the sensitivity (also called true positive rate or hit rate) and the specificity (true negative rate) as:Sensitivity = tp/posSpecificity = tn/negwhere pos=tp+fn is the total number of positive examples and neg=tn+fp the total number of negative examples.
presentation title
presentation title
presentation title
0,n
1,n
0,n
1,n
0,1
0,n
0,n
0,n
1,1
1,1
1,n
0,n
0,n
1,1
1,n
1,n
1,n
1,n
0,n
1,n
1,1
0,n
1,n
1,1
0,n
1,1
0,n
1,1
0,n
1,n
0,n
1,1
1,1
0,n
1,n
1,1
1,n
(1,1)
1,1
0,n
1,n
0,n
1,1
0,n
0,n
0,1
1,1
1,n
1,n
0,1
0,n
1,1
1,1
0,1
0,n
Heritage tiers
Heritage offre commerciale
0,n
1,n
1,1
0,n
(1,1)
0,n
0,n
0,n
0,1
0,1
0,n
1,n
1,n
0,n
1,n
0,n
(1,1)
Fu appartient type FU
1,1
1,n
Offre
Id offre
Libell offre
Produit & Service
Id PS
Date fin validit du P&S
Date dbut validit du P&S
Date cration du P&S
Libell P&S
Identit Tiers
Id identit tiers
Login
Type identit tiers
O compose de PS
Elment De Parc
Id EDP
Date dernire utilisation EDP
Date premire utilisation EDP
Modle Conceptuel de Donnes
Modle : MCD PAC_v4
Package :
Diagramme : Tiers Services
Auteur : claudebe
Date : 14/06/2005
Version :
PS a pour FU
T utilise IT
EDP souscrit ds O
Date dbut souscription offre
Date fin souscription offre
D
D
CRU concerne FU
Gamme
Id gamme
Libell gamme
Date cration gamme
Date fin de gamme
G compose de PS
Fonction Usage
Id fonction d'usage
Libll fonction usage
T dtient EDP
Date dbut dtention EDP
Date fin dtention EDP
D
D
Compte Facturation
Id compte facturation
Date dbut validit compte facturation
Date fin validit compte facturation
F mise pour CF
Compte Rendu Usage
Id compte rendu usage
Date dbut CRU
Date fin CRU
Volume descendant CRU
Volume montant CRU
Type transmission
IT gnre CRU
CRU gnr par EDP
Ligne Facture
Id ligne de facture
Ligne affiche sur facture
Montant HT
Montant TTC
Type Ligne Facture
Id type ligne facture
Libell type ligne facture
Facture
Id facture
Date chance facture
LF correspond EDP
LF compose F
EDP factur sur CF
Tiers
Id tiers
Prnom tiers PP
Nom tiers PP
Nom marital PP
Genre PP
Date naissance tiers PP
Date cration tiers
Date clture tiers
Date modification tiers
Type Tiers
T a pour relation avec T
Foyer
Id foyer
Date cration foyer
Date fin foyer
Nb personnes foyer
Adresse
Id adresse
Code postal distribution
Commune
Nb habitants commune
Dpartement
F a pour A
Date dbut adresse
Date fin adresse
D
D
T a pour F
Date dbut appartenance foyer
Date fin appartenance foyer
Role tiers ds foyer
D
D
VA1
Type Relation Tiers
Id type relation
Date cration type de relation tiers
Libell type relation tiers
Statut Oprateur
Id statut oprateur
Libell statut oprateur
Operateur
Id oprateur
Libell oprateur
T a pour S
Date dbut statut tiers
Date fin statut tiers
D
D
CSP
Id CSP 350
Libell CSP 350
Id CSP 23
Libell CSP 23
Id CSP 5
Libell CSP 5
T a pour CSP
LF a pour TLF
Classification Offre
Id classification offre
Libell classification offre
O positionne ds C
CO hirarchie
Groupe de CRU
Id groupe de CRU
CRU appartient la CCRU
Cercle Relationnel
Id CR
Libll cercle relationnel
CRU a pour OCR
CRU a pour DCR
Coordonnes Tiers
Id coordonne tiers
Date cration coordonne
Libell coordonne tiers
T titulaire CT
C correspond M
Donnes payeur
Inscription fichier contentieux
Nb dossiers recouvrement actifs
Nb dossiers rclamation actifs
Nb dossiers recouvrement
Nb dossiers rclamation
Niveau risque courant
Niveau risque prcdent
Classe de risque
Id classe risque
Libell classe risque
Libell court classe risque
Niveau risque minimum
Niveau risque maximum
T a pour CR
Date dbut tiers ds classe risque
Date fin tiers ds classe risque
D
D
Offre compose
Id offre compose
Libell offre compose
Offre commerciale
Id offre commerciale
Libell offre commerciale
Date cration offre
Date clture offre
O fait partie OC
Date dbut rattachement offre
Date fin rattachement offre
D
D
EDP correspond PS
Positionnement classification
Id positionnement
Libell positionnement
P dans O
P hirarchie
CRU Enchainement
Mdia
Id mdia
Libell mdia
EDP a EU
mois
valeur
VA6
N10
T payeur du CF
DP pour O
Etat Usage
Id EU
libell tat usage
Type de fonction d'usage
id type FU
lib typ FU
Id customerzip codeNb call/monthNb calls/hourNb calls/month,weekday,hours,service
KDD Cup Participation By Year
45
57
24
31
136
18
57
102
37
68
95
128
453
0
50
100
150
200
250
300
350
400
450
500
1997199819992000200120022003200420052006200720082009
Year
