Discovery Science: 20th International Conference

This book constitutes the proceedings of the 20th International Conference on Discovery Science, DS 2017, held in Kyoto, Japan, in October 2017, co-located with the International Conference on Algorithmic Learning Theory, ALT 2017.

The 18 revised full papers presented together with 6 short papers and 2 invited talks in this volume were carefully reviewed and selected from 42 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in topical sections on machine learning: online learning, regression, label classification, deep learning, feature selection, recommendation system; and knowledge discovery: recommendation system, community detection, pattern mining, misc.

Table of Contents

Part 1 Online Learning
Chapter 1 Context-Based Abrupt Change Detection and Adaptation for Categorical Data Streams
Chapter 2 A New Adaptive Learning Algorithm and Its Application to Online Malware Detection
Chapter 3 Real-Time Validation of Retail Gasoline Prices

Part 2 Regression
Chapter 4 General Meta-Model Framework for Surrogate-Based Numerical Optimization
Chapter 5 Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression
Chapter 6 Differentially Private Empirical Risk Minimization with Input Perturbation

Part 3 Label Classification
Chapter 7 On a New Competence Measure Applied to the Dynamic Selection of Classifiers Ensemble
Chapter 8 Multi-label Classification Using Random Label Subset Selections
Chapter 9 Option Predictive Clustering Trees for Hierarchical Multi-label Classification

Part 4 Deep Learning
Chapter 10 Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction
Chapter 11 An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling

Part 5 Feature Selection
Chapter 12 Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Feature Selection
Chapter 13 Feature Ranking for Multi-target Regression with Tree Ensemble Methods

Part 6 Recommendation System
Chapter 14 Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers

Part 7 Community Detection
Chapter 15 Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method
Chapter 16 Discovering Hidden Knowledge in Carbon Emissions Data: A Multilayer Network Approach
Chapter 17 Topic Extraction on Twitter Considering Author’s Role Based on Bipartite Networks

Part 8 Pattern Mining
Chapter 18 Mining Strongly Closed Itemsets from Data Streams
Chapter 19 Extracting Mutually Dependent Multisets

Part 9 Bioinformatics
Chapter 20 LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks
Chapter 21 Discovery of Salivary Gland Tumors’ Biomarkers via Co-Regularized Sparse-Group Lasso

Part 10 Knowledge Discovery
Chapter 22 Measuring the Inspiration Rate of Topics in Bibliographic Networks
Chapter 23 Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data
Chapter 24 Fusion Techniques for Named Entity Recognition and Word Sense Induction and Disambiguation