{"product_id":"9783030615260","title":"Discovery Science : 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings (Lecture Notes in Computer Science 12323) (1st ed. 2020 2020 xxi, 706 S. 80 SW-Abb., 147 Farbabb. 235 mm)","description":"\u003cp\u003eThis book constitutes the proceedings of the 23rd International Conference on Discovery Science, DS 2020, which took place during October 19-21, 2020. The conference was planned to take place in Thessaloniki, Greece, but had to change to an online format due to the COVID-19 pandemic. \u003c\/p\u003e \u003cp\u003eThe 26 full and 19 short papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eClassification\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eEvaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach.- Mitigating Discrimination in Clinical Machine Learning Decision Support using Algorithmic Processing Techniques.- WeakAL: Combining Active Learning and Weak Supervision.- \u003cb\u003eClustering\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eConstrained Clustering via Post-Processing.- Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso's Artworks.- Dynamic Incremental Semi-Supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction.- Iterative Multi-Mode Discretization: Applications to Co-Clustering.- \u003cb\u003eData and Knowledge Representation\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eCOVID-19 Therapy Target Discovery with Context-aware Literature Mining.- Semantic Annotation of Predictive Modelling Experiments.- Semantic Description of Data Mining Datasets: An Ontology-based Annotation Schema.- \u003cb\u003eData Streams\u003c\/b\u003e.- FABBOO - Online Fairness-aware Learning under Class Imbalance.- FEAT: A Fairness-enhancing andConcept-adapting Decision Tree Classifer.- Unsupervised Concept Drift Detection using a Student{Teacher Approach.- \u003cb\u003eDimensionality Reduction and Feature Selection\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eAssembled Feature Selection For Credit Scoring in Micro nance With Non-Traditional Features.- Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite.- Nets versus Trees for Feature Ranking and Gene Network Inference.- Pathway Activity Score Learning Algorithm for Dimensionality Reduction of Gene Expression Data.- Machine learning for Modelling and Understanding in Earth Sciences.- \u003cb\u003eDistributed Processing\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eBalancing between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings.- DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain.- Investigating Parallelization of MAML.- \u003cb\u003eEnsembles\u003c\/b\u003e.- Extreme Algorithm Selection with Dyadic Feature Representation.- Federated Ensemble Regression using Classification.- One-Class Ensembles for Rare Genomic Sequences Identification.- \u003cb\u003eExplainable and Interpretable Machine Learning\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eExplaining Sentiment Classi cation with Synthetic Exemplars and Counter-Exemplars.- Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology.- Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction.- Predicting and Explaining Privacy Risk Exposure in Mobility Data.- \u003cb\u003eGraph and Network Mining\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eMaximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links.- On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective.- Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors.- \u003cb\u003eMulti-Target Models\u003c\/b\u003e.- Extreme Gradient Boosted Multi-label Trees for Dynamic ClassifierChains.- Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems.- Missing Value Imputation with MERCS: a Faster Alternative to MissForest.- Multi-Directional Rule Set Learning.- On Aggregation in Ensembles of Multilabel Classifiers.- \u003cb\u003eNeural Networks and Deep Learning\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eAttention in Recurrent Neural Networks for Energy Disaggregation.- Enhanced Food Safety Through Deep Learning for Food Recalls Prediction.- Machine learning for Modelling and Understanding in Earth Sciences.- FairNN - Conjoint Learning of Fair Representations for Fair Decisions.- Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution.- \u003cb\u003eSpatial, Temporal and Spatiotemporal Data\u003c\/b\u003e.- Detecting Temporal Anomalies in Business Processes using Distance-based Methods.- Mining Constrained Regions of Interest: An Optimization Approach.- Mining Disjoint Sequential Pattern Pairs from Tourist Traje\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER INTERNATIONAL PUBLISHING;","offers":[{"title":"Default Title","offer_id":48820206141675,"sku":"00000_00000_00000_00000","price":183.1,"currency_code":"SGD","in_stock":true}],"url":"https:\/\/kinokuniya.com.sg\/ja\/products\/9783030615260","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}