Machine Learning and Knowledge Discovery in Databases

1. Front Matter

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2. Privacy and Security

   1. AUDIO: An Integrity \(\underline{Audi}\)ting Framework of \(\underline{O}\)utlier-Mining-as-a-Service Systems

      • Ruilin Liu, Hui (Wendy) Wang, Anna Monreale, Dino Pedreschi, Fosca Giannotti, Wenge Guo

      Pages 1-18

   2. Differentially Private Projected Histograms: Construction and Use for Prediction

      • Staal A. Vinterbo

      Pages 19-34

   3. Fairness-Aware Classifier with Prejudice Remover Regularizer

      • Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma

      Pages 35-50

3. Rankings and Recommendations

   1. A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com

      • Evan Kirshenbaum, George Forman, Michael Dugan

      Pages 51-66

   2. Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback

      • Balázs Hidasi, Domonkos Tikk

      Pages 67-82

   3. Probability Estimation for Multi-class Classification Based on Label Ranking

      • Weiwei Cheng, Eyke Hüllermeier

      Pages 83-98

4. Reinforcement Learning and Planning

   1. Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery

      • N. Kemal Ure, Alborz Geramifard, Girish Chowdhary, Jonathan P. How

      Pages 99-115

   2. APRIL: Active Preference Learning-Based Reinforcement Learning

      • Riad Akrour, Marc Schoenauer, Michèle Sebag

      Pages 116-131

   3. Autonomous Data-Driven Decision-Making in Smart Electricity Markets

      • Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins

      Pages 132-147

   4. Bayesian Nonparametric Inverse Reinforcement Learning

      • Bernard Michini, Jonathan P. How

      Pages 148-163

   5. Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic

      • Truong-Huy Dinh Nguyen, Wee-Sun Lee, Tze-Yun Leong

      Pages 164-179

   6. Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

      • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari

      Pages 180-194

   7. Learning Policies for Battery Usage Optimization in Electric Vehicles

      • Stefano Ermon, Yexiang Xue, Carla Gomes, Bart Selman

      Pages 195-210

   8. Policy Iteration Based on a Learned Transition Model

      • Vivek Ramavajjala, Charles Elkan

      Pages 211-226

   9. Structured Apprenticeship Learning

      • Abdeslam Boularias, Oliver Krömer, Jan Peters

      Pages 227-242

5. Rule Mining and Subgroup Discovery

   1. A Bayesian Approach for Classification Rule Mining in Quantitative Databases

      • Dominique Gay, Marc Boullé

      Pages 243-259

   2. A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

      • Iyad Batal, Gregory Cooper, Milos Hauskrecht

      Pages 260-276

   3. Generic Pattern Trees for Exhaustive Exceptional Model Mining

      • Florian Lemmerich, Martin Becker, Martin Atzmueller

      Pages 277-292

6. Semi-Supervised and Transductive Learning

   1. Bidirectional Semi-supervised Learning with Graphs

      • Tomoharu Iwata, Kevin Duh

      Pages 293-306

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

• Bayesian network classifiers
• data mining
• hypergraphs
• social media
• tree search

• Intelligent Systems Laboratory, University of Bristol, Bristol, UK

  Peter A. Flach, Tijl Bie, Nello Cristianini

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