Learning from Label Proportions by Optimizing Cluster Model Selection

详细信息    查看全文

• 作者：Marco Stolpe (1) marco.stolpe@tu-dortmund.de
  Katharina Morik (1) katharina.morik@tu-dortmund.de
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：6913
• 期：1
• 页码：349-364
• 全文大小：357.3 KB
• 参考文献：1. Aggarwal, C.C., Wolf, J.L., Yu, P.S., Procopiuc, C., Park, J.S.: Fast algorithms for projected clustering. In: Proc. of the Int. Conf. on Management of Data, SIGMOD 1999, pp. 61–72. ACM, New York (1999)
  2. Aha, D.: Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms. Int. J. of Man-Machine Studies 36(2), 267–287 (1992)
  3. Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
  4. Breimann, L.: Random forests. Machine Learning 45, 5–32 (2001)
  5. Chapelle, O., Sch枚lkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
  6. Chen, S., Liu, B., Qian, M., Zhang, C.: Kernel k-Means based framework for aggregate outputs classification. In: Proc. of the Int. Conf. on Data Mining Workshops (ICDMW), pp. 356–361 (2009)
  7. Dara, R., Kremer, S., Stacey, D.: Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Proc. of the 2002 Int. Joint Conf. on Neural Networks (IJCNN), vol. 3, pp. 2237–2242 (2002)
  8. Demiriz, A., Bennett, K., Bennett, K.P., Embrechts, M.J.: Semi-supervised clustering using genetic algorithms. In: Proc. of Artif. Neural Netw. in Eng (ANNIE), pp. 809–814. ASME Press (1999)
  9. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
  10. Dhillon, I., Guan, Y., Kulis, B.: Kernel k-Means: spectral clustering and normalized cuts. In: Proc. of the 10th Int. Conf. on Knowl. Discov. and Data Mining, SIGKDD 2004, pp. 551–556. ACM, New York (2004)
  11. Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proc. of the 20th Int. Conf. on Machine Learning (ICML) (2003)
  12. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the 2nd Int. Conf. on Knowl. Discov. and Data Mining, pp. 226–231. AAAI Press, Menlo Park (1996)
  13. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Statistics, 2nd edn. Springer, Heidelberg (2009)
  14. John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proc. of the 11th Conf. on Uncertainty in Artif. Int., pp. 338–345. Morgan Kaufmann, San Francisco (1995)
  15. Kueck, H., de Freitas, N.: Learning about individuals from group statistics. In: Uncertainty in Artif. Int. (UAI), pp. 332–339. AUAI Press, Arlington (2005)
  16. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Symp. Math. Stat. & Prob., pp. 281–297 (1967)
  17. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
  18. Musicant, D.R., Christensen, J.M., Olson, J.F.: Supervised learning by training on aggregate outputs. In: Proc. of the 7th Int. Conf. on Data Mining (ICDM), pp. 252–261. IEEE Computer Society, Washington, DC, USA (2007)
  19. Quadrianto, N., Smola, A.J., Caetano, T.S., Le, Q.V.: Estimating labels from label proportions. J. Mach. Learn. Res. 10, 2349–2374 (2009)
  20. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
  21. R眉ping, S.: SVM classifier estimation from group probabilities. In: Proc. of the 27th Int. Conf. on Machine Learning (ICML) (2010)
  22. Vapnik, V.: The nature of statistical learning theory, 2nd edn. Springer, New York (1999)
  23. Witten, I.H., Eibe, F., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. In: Data Management Systems, 3rd edn. Elsevier, Inc., Burlington (2011)
• 作者单位：1. Artificial Intelligence Group, Technical University of Dortmund, Baroper Strasse 301, 44227 Dortmund, Germany
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In a supervised learning scenario, we learn a mapping from input to output values, based on labeled examples. Can we learn such a mapping also from groups of unlabeled observations, only knowing, for each group, the proportion of observations with a particular label? Solutions have real world applications. Here, we consider groups of steel sticks as samples in quality control. Since the steel sticks cannot be marked individually, for each group of sticks it is only known how many sticks of high (low) quality it contains. We want to predict the achieved quality for each stick before it reaches the final production station and quality control, in order to save resources. We define the problem of learning from label proportions and present a solution based on clustering. Our method empirically shows a better prediction performance than recent approaches based on probabilistic SVMs, Kernel k-Means or conditional exponential models.