Discovery Science 2020
Channel of the Discovery Science Conference
Interpretable Machine Learning with Bitonic GAMs & Automatic Feature Construction
Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction
On Aggregation in Ensembles of Multilabel Classifiers
Multi-directional Rule Set Learning
Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars
Attention in Recurrent Neural Networks for Energy Disaggregation
Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso’s Artworks
Semantic Annotation of Predictive Modelling Experiments
Constrained Clustering via Post-processing
Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite
Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema
FairNN - Conjoint Learning of Fair Representations for Fair Decisions
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features
Missing Value Imputation with MERCS: A Faster Alternative to MissForest
Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data
Balancing Between Scalability and Accuracy in Time-Series Classification for Stream & Batch Settings
WeakAL: Combining Active Learning and Weak Supervision
Unsupervised Concept Drift Detection Using a Student–Teacher Approach
COVID-19 Therapy Target Discovery with Context-Aware Literature Mining
Predicting and Explaining Privacy Risk Exposure in Mobility Data
Federated Ensemble Regression Using Classification
Generating Explainable and Effective Data Descriptors Using Relational Learning
Mitigating Discrimination in Clinical Machine Learning Decision Support Using Algorithmic Techniques
Nets Versus Trees for Feature Ranking and Gene Network Inference
Time Series Regression in Professional Road Cycling
Mining Constrained Regions of Interest: An Optimization Approach
Iterative Multi-mode Discretization: Applications to Co-clustering
Enhanced Food Safety Through Deep Learning for Food Recalls Prediction
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach
One-Class Ensembles for Rare Genomic Sequences Identification