Advances in Knowledge Discovery and Data Mining

1. Front Matter

   Pages I-XXIV

   PDF

2. Classification

   1. Front Matter

      Pages 1-1

      PDF

   2. Joint Classification with Heterogeneous Labels Using Random Walk with Dynamic Label Propagation

      • Yongxin Liao, Shenxi Yuan, Jian Chen, Qingyao Wu, Bin Li

      Pages 3-13

   3. Hybrid Sampling with Bagging for Class Imbalance Learning

      • Yang Lu, Yiu-ming Cheung, Yuan Yan Tang

      Pages 14-26

   4. Sparse Adaptive Multi-hyperplane Machine

      • Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Phung

      Pages 27-39

   5. Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior

      • Qinzhe Zhang, Qin Zhang, Guodong Long, Peng Zhang, Chengqi Zhang

      Pages 40-51

   6. Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning

      • Yuxun Zhou, Jae Yeon Baek, Dan Li, Costas J. Spanos

      Pages 52-64

   7. Locally Weighted Ensemble Learning for Regression

      • Man Yu, Zongxia Xie, Hong Shi, Qinghua Hu

      Pages 65-76

   8. Reliable Confidence Predictions Using Conformal Prediction

      • Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström

      Pages 77-88

   9. Grade Prediction with Course and Student Specific Models

      • Agoritsa Polyzou, George Karypis

      Pages 89-101

   10. Flexible Transfer Learning Framework for Bayesian Optimisation

       • Tinu Theckel Joy, Santu Rana, Sunil Kumar Gupta, Svetha Venkatesh

       Pages 102-114

   11. A Simple Unlearning Framework for Online Learning Under Concept Drifts

       • Sheng-Chi You, Hsuan-Tien Lin

       Pages 115-126

   12. User-Guided Large Attributed Graph Clustering with Multiple Sparse Annotations

       • Jianping Cao, Senzhang Wang, Fengcai Qiao, Hui Wang, Feiyue Wang, Philip S. Yu

       Pages 127-138

   13. Early-Stage Event Prediction for Longitudinal Data

       • Mahtab J. Fard, Sanjay Chawla, Chandan K. Reddy

       Pages 139-151

   14. Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework

       • Cheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashely et al.

       Pages 152-164

   15. Shot Boundary Detection Using Multi-instance Incremental and Decremental One-Class Support Vector Machine

       • Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford

       Pages 165-176

   16. Will I Win Your Favor? Predicting the Success of Altruistic Requests

       • Hsun-Ping Hsieh, Rui Yan, Cheng-Te Li

       Pages 177-188

3. Feature Extraction and Pattern Mining

   1. Front Matter

      Pages 189-189

      PDF

   2. Unsupervised and Semi-supervised Dimensionality Reduction with Self-Organizing Incremental Neural Network and Graph Similarity Constraints

      • Zhiyang Xiang, Zhu Xiao, Yourong Huang, Dong Wang, Bin Fu, Wenjie Chen

      Pages 191-202

   3. Cross-View Feature Hashing for Image Retrieval

      • Wei Wu, Bin Li, Ling Chen, Chengqi Zhang

      Pages 203-214

This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016.

The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.

• cloud-based high performance data mining
• cyber-security
• distributed data mining
• healthcare
• ubiquitous knowledge discovery
• agent-based data mining
• anomaly detection
• bioinformatics
• data mining
• data warehousing
• human factors
• interactive mining
• intrusion detection
• marketing
• OLAP
• opinion mining
• parallel data mining
• security
• social factors
• statistical methods

• The University of Melbourne, Melbourne, Australia

  James Bailey

• The University of Texas at Dallas, Richardson, USA

  Latifur Khan

• Osaka University, Osaka, Japan

  Takashi Washio

• University of Auckland, Auckland, New Zealand

  Gill Dobbie

• Shenzhen University, Shenzhen, China

  Joshua Zhexue Huang

• Massey University, Auckland, New Zealand

  Ruili Wang

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