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  • Conference proceedings
  • © 2012

Advances in Knowledge Discovery and Data Mining, Part I

16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June1, 2012, Proceedings, Part I

Editors:

  1. Pang-Ning Tan

    1. Department of Computer Science, Michigan State University, East Lansing, USA

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  2. Sanjay Chawla

    1. School of Information Technologies, University of Sydney, Sydney, Australia

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  3. Chin Kuan Ho

    1. Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, Cyberjaya, Malaysia

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  4. James Bailey

    1. Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7301)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Conference series link(s): PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining

Conference proceedings info: PAKDD 2012.

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Table of contents (50 papers)

  1. Front Matter

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  2. Supervised Learning: Active, Ensemble, Rare-Class and Online

    1. Time-Evolving Relational Classification and Ensemble Methods

      • Ryan Rossi, Jennifer Neville
      Pages 1-13

    2. Active Learning for Hierarchical Text Classification

      • Xiao Li, Da Kuang, Charles X. Ling
      Pages 14-25

    4. A Novel Weighted Ensemble Technique for Time Series Forecasting

      • Ratnadip Adhikari, R. K. Agrawal
      Pages 38-49

    5. Techniques for Efficient Learning without Search

      • Houssam Salem, Pramuditha Suraweera, Geoffrey I. Webb, Janice R. Boughton
      Pages 50-61

    7. Two-View Online Learning

      • Tam T. Nguyen, Kuiyu Chang, Siu Cheung Hui
      Pages 74-85

    9. Neighborhood Random Classification

      • Djamel Abdelkader Zighed, Diala Ezzeddine, Fabien Rico
      Pages 98-108

    10. SRF: A Framework for the Study of Classifier Behavior under Training Set Mislabeling Noise

      • Katsiaryna Mirylenka, George Giannakopoulos, Themis Palpanas
      Pages 109-121

    11. Building Decision Trees for the Multi-class Imbalance Problem

      • T. Ryan Hoens, Qi Qian, Nitesh V. Chawla, Zhi-Hua Zhou
      Pages 122-134

    12. Scalable Random Forests for Massive Data

      • Bingguo Li, Xiaojun Chen, Mark Junjie Li, Joshua Zhexue Huang, Shengzhong Feng
      Pages 135-146

    13. Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data

      • Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Mark Junjie Li, Yunming Ye
      Pages 147-158

    14. Learning Tree Structure of Label Dependency for Multi-label Learning

      • Bin Fu, Zhihai Wang, Rong Pan, Guandong Xu, Peter Dolog
      Pages 159-170

    15. Multiple Instance Learning for Group Record Linkage

      • Zhichun Fu, Jun Zhou, Peter Christen, Mac Boot
      Pages 171-182

    16. Incremental Set Recommendation Based on Class Differences

      • Yasuyuki Shirai, Koji Tsuruma, Yuko Sakurai, Satoshi Oyama, Shin-ichi Minato
      Pages 183-194

    17. Active Learning for Cross Language Text Categorization

      • Yue Liu, Lin Dai, Weitao Zhou, Heyan Huang
      Pages 195-206

    18. Evasion Attack of Multi-class Linear Classifiers

      • Han Xiao, Thomas Stibor, Claudia Eckert
      Pages 207-218

    19. Foundation of Mining Class-Imbalanced Data

      • Da Kuang, Charles X. Ling, Jun Du
      Pages 219-230
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Other Volumes

  1. Advances in Knowledge Discovery and Data Mining

  2. Advances in Knowledge Discovery and Data Mining

  3. Emerging Trends in Knowledge Discovery and Data Mining

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About this book

The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning: active, ensemble, rare-class and online; unsupervised learning: clustering, probabilistic modeling in the first volume and on pattern mining: networks, graphs, time-series and outlier detection, and data manipulation: pre-processing and dimension reduction in the second volume.
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Keywords

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Editors and Affiliations

  • Department of Computer Science, Michigan State University, East Lansing, USA

    Pang-Ning Tan

  • School of Information Technologies, University of Sydney, Sydney, Australia

    Sanjay Chawla

  • Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, Cyberjaya, Malaysia

    Chin Kuan Ho

  • Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    James Bailey

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Bibliographic Information

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Access via your institution

Buy it now

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

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