Discovery Science

1. Front Matter

   Pages I-XXI

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2. Pattern Mining and Rules

   1. Front Matter

      Pages 1-1

      PDF

   2. Exceptional Preferences Mining

      • Cláudio Rebelo de Sá, Wouter Duivesteijn, Carlos Soares, Arno Knobbe

      Pages 3-18

   3. Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships

      • Guillaume Bosc, Jérôme Golebiowski, Moustafa Bensafi, Céline Robardet, Marc Plantevit, Jean-François Boulicaut et al.

      Pages 19-34

   4. InterSet: Interactive Redescription Set Exploration

      • Matej Mihelčić, Tomislav Šmuc

      Pages 35-50

   5. Expect the Unexpected – On the Significance of Subgroups

      • Matthijs van Leeuwen, Antti Ukkonen

      Pages 51-66

   6. Min-Hashing for Probabilistic Frequent Subtree Feature Spaces

      • Pascal Welke, Tamás Horváth, Stefan Wrobel

      Pages 67-82

3. Structured Output Prediction

   1. Front Matter

      Pages 83-83

      PDF

   2. STIFE: A Framework for Feature-Based Classification of Sequences of Temporal Intervals

      • Leon Bornemann, Jason Lecerf, Panagiotis Papapetrou

      Pages 85-100

   3. Approximating Numeric Role Fillers via Predictive Clustering Trees for Knowledge Base Enrichment in the Web of Data

      • Giuseppe Rizzo, Claudia d’Amato, Nicola Fanizzi, Floriana Esposito

      Pages 101-117

   4. Option Predictive Clustering Trees for Multi-target Regression

      • Aljaž Osojnik, Sašo Džeroski, Dragi Kocev

      Pages 118-133

   5. HSIM: A Supervised Imputation Method for Hierarchical Classification Scenario

      • Leandro R. Galvão, Luiz H. C. Merschmann

      Pages 134-148

4. Applications

   1. Front Matter

      Pages 149-149

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   2. Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype

      • Sebastian Kauschke, Johannes Fürnkranz, Frederik Janssen

      Pages 151-166

   3. Predicting Bug-Fix Time: Using Standard Versus Topic-Based Text Categorization Techniques

      • Pasquale Ardimento, Massimo Bilancia, Stefano Monopoli

      Pages 167-182

   4. Predicting Wildfires

      • Mariana Oliveira, Luís Torgo, Vítor Santos Costa

      Pages 183-197

   5. Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach

      • Juan G. Colonna, João Gama, Eduardo F. Nakamura

      Pages 198-212

   6. Evolution Analysis of Call Ego-Networks

      • Shazia Tabassum, João Gama

      Pages 213-225

5. Ensemble Learning

   1. Front Matter

      Pages 227-227

      PDF

   2. Ensemble Diversity in Evolving Data Streams

      • Dariusz Brzezinski, Jerzy Stefanowski

      Pages 229-244

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2016, held in banff, AB, Canada in October 2015. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 60 submissions.The conference focuses on following topics: Advances in the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scientific domains.

• data mining
• evolving networks
• knowledge discovery
• online social networks
• pattern mining
• algorithm analysis and problem complexity

• Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

  Toon Calders

• Università degli Studi di Bari Aldo Moro, Bari, Italy

  Michelangelo Ceci

• Bari, Italy

  Donato Malerba

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