Results

KDD Cup 2009: Customer relationship prediction

Winners of KDD Cup 2009: Fast Track

  • First Place: IBM Research
    Ensemble Selection for the KDD Cup Orange Challenge
  • First Runner Up: ID Analytics, Inc
    KDD Cup Fast Scoring on a Large Database
  • Second Runner Up: Old dogs with new tricks (David Slate, Peter W. Frey)

Winners of KDD Cup 2009: Slow Track

  • First Place: University of Melbourne
    University of Melbourne entry
  • First Runner Up: Financial Engineering Group, Inc. Japan
    Stochastic Gradient Boosting
  • Second Runner Up: National Taiwan University, Computer Science and Information Engineering
    Fast Scoring on a Large Database using regularized maximum entropy model, categorical/numerical balanced AdaBoost and selective Naive Bayes

Full Results: Fast Track

Rank Team Name Method AUC
Churn Appetency Upselling Score
1 IBM Research Final Submission 0.7611 0.8830 0.9038 0.8493
2 ID Analytics, Inc DT 0.7565 0.8724 0.9056 0.8448
3 Old dogs with new tricks Our own method 0.7541 0.8740 0.9050 0.8443
4 Crusaders Joint Score Technique 0.7569 0.8688 0.9034 0.8430
5 Financial Engineering Group, Inc. Japan boosting 0.7498 0.8732 0.9057 0.8429
6 LatentView Analytics Boosting 0.7579 0.8670 0.9034 0.8428
7 Data Mining Logistic 0.7580 0.8659 0.9034 0.8424
8 StatConsulting (K.Ciesielski, M.Sapinski, M.Tafil) AdvancedMiner 0.7544 0.8723 0.8997 0.8421
9 Sigma Decision Tree Algo 0.7568 0.8644 0.9034 0.8415
10 Analytics CART 0.7564 0.8644 0.9034 0.8414
11 Ming Li & Yuwei Zhang me 0.7507 0.8683 0.9050 0.8413
12 Hungarian Academy of Sciences fri4 0.7496 0.8683 0.9042 0.8407
13 Oldham Athletic Reserves tiberius10 0.7492 0.8699 0.9026 0.8406
14 Swetha Logistic 0.7550 0.8659 0.8996 0.8401
15 VladN vnf8c 0.7415 0.8692 0.9012 0.8373
16 VADIS Bagging 0.7474 0.8631 0.8994 0.8366
17 brendano random forests (res11) 0.7468 0.8627 0.9003 0.8366
18 commendo 1 before noon 0.7381 0.8693 0.8988 0.8354
19 FEG CTeam Boosting 0.7389 0.8616 0.9011 0.8338
20 Vadis Team 2 Best final 0.7442 0.8568 0.8996 0.8335
21 National Taiwan University, Computer Science and Information Engineering all2 0.7428 0.8679 0.8890 0.8332
22 Kranf TIM 0.7463 0.8478 0.8980 0.8307
23 Neo Metrics final2 0.7454 0.8449 0.8994 0.8299
24 ooo 10-3 0.7427 0.8520 0.8920 0.8289
25 TonyM mymethod5 0.7397 0.8481 0.8988 0.8289
26 AIIALAB ensemble 0.7413 0.8458 0.8969 0.8280
27 Uni Melb hfinal 0.7087 0.8669 0.8996 0.8251
28 Christian Colot My GoldMiner 0.7183 0.8577 0.8958 0.8240
29 Céline Theeuws final 0.7346 0.8476 0.8835 0.8219
30 m&m final test 0.7218 0.8423 0.8924 0.8189
31 Predictive Analytics Logistic 0.7131 0.8336 0.8917 0.8128
32 DKW NN / Logistic Regression on Laptop 0.6980 0.8449 0.8928 0.8119
33 NICAL Dys 0.7108 0.8461 0.8707 0.8092
34 UW eq+uneq 0.6804 0.8531 0.8815 0.8050
35 Prem Swaroop thmdkd4 0.6972 0.8384 0.8794 0.8050
36 Dr. Bunsen Honeydew submission #004 0.7048 0.8235 0.8760 0.8015
37 dodio L2 0.7179 0.8474 0.8356 0.8003
38 FEG D TEAM mix2 0.6997 0.8139 0.8824 0.7987
39 minos rdf 0.6828 0.8233 0.8698 0.7920
40 M Release1 0.7289  
Copyrights © 2016 All Rights Reserved - SIGKDD
ACM Code of Conduct