Rough Sets
Springer International Publishing (Verlag)
978-3-031-50958-2 (ISBN)
The 43 full papers included in this book were carefully reviewed and selected from 83 submissions. They were organized in topical sections as follows: Rough Set Models, Foundations, Three-way Decisions, Granular Models, Distances and Similarities, Hybrid Approaches, Applications, Cybersecurity and IoT.
Rough Set Models.- Selected approaches to conflict analysis inspired by the Pawlak model - case study.- Multi-heuristic Induction of Decision Rules.- Algebraic formulations and geometric interpretations of decision-theoretic rough sets.- Reduction of binary attributes: rough set theory versus formal concept analysis.- An Acceleration Method for Attribute Reduction Based on Attribute Synthesis.- Attribute Reduction Based on the Multi-annulus Model.- Foundations.- Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-valued Decisions from Closed Classes.- Paraconsistent Logics: A Survey Focussing on the Rough Set Approach.- Hexagons of Opposition in Linguistic Three-Way Decisions.- Algebraic Models for Qualified Aggregation in General Rough Sets, and Reasoning Bias Discovery.- Two-sorted Modal Logic for Formal and Rough Concepts.- Kryszkiewicz's Relation for Indiscernibility of Objects in Data Tables Containing Missing Values.- Algebraic, Topological, and Mereological Foundations of Existential Granules.- Aggregation Operators on Shadowed Sets Deriving from Conditional Events and Consensus Operators.- Pawlak, Belnap and the magical number seven.- Three-way Decisions.- Three-way conflict analysis for three-valued situation tables with rankings and reference tuples.- Three-way social network analysis: Triadic measures at three levels.- Cognitive and Social Decision Making: Three-way Decision Perspectives.- New models of three-way conflict analysis for incomplete situation tables.- Granular-ball Three-way Decision- Granular Models.- Unsupervised KeyPhrase Extraction Based on Multi-granular Semantics Feature Fusion.- Multi-Granularity Feature Fusion for Transformer-based Single Object Tracking.- A Multi-Granularity Network for Time Series Forecasting on Multivariate Time Series Data.- Adaptive Multi-Granularity Aggregation Transformer for Image Captioning.- A Causal Disentangled Multi-Granularity Graph Classification Method.- Distances and Similarities.- Towards ML Explainability with Rough Sets, Clustering, and Dimensionality Reduction.- Decision rule clustering - Comparison of the algorithms.- Classifying token frequencies using angular Minkowski $p$-distance.- On kNN class weights for optimising G-mean and F1-score.- Searching of potentially anomalous signals in cosmic-ray particle tracks images using rough k-means clustering combined with eigendecomposition-derived embedding.- Hybrid Approaches.- Crisp-Fuzzy Concept Lattice Based on Interval-Valued Fuzzy Sets.- Normal fuzzy three-way decision based on prospect theory.- On Several New Dempster-Shafer-inspired Uncertainty Measures Applicable for Active Learning.- Rough Fuzzy Concept Analysis via Multilattice.- Applications.- Clustering methods for adaptive e-commerce user interfaces.- Application of federated learning to prediction of patient mortality in vasculitis disease.- A Novel Hybrid Wind Speed Interval Prediction Model using Rough Stacked Autoencoder and LSTM.- Navigational Strategies for Mobile Robots Using Rough Mereological Potential Fields and Weighted Distance to Goal.- Handling intra-class dissimilarity and inter-class similarity for imbalanced skin lesion image classification.- Link Prediction for Attribute and Structure Learning Based on Attention Mechanism.- Cybersecurity and IoT.- A New Data Model for Behavioral Based Anomaly Detection in IoT Device Monitoring.- Preventing Text Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key.- Improving Detection Efficiency: Optimizing Block Size in the Local Outlier Factor (LOF) Algorithm.
| Erscheinungsdatum | 03.01.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | LIX, 644 p. 150 illus., 98 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1047 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Sozialwissenschaften | |
| Schlagworte | Artificial Intelligence • attribute reduction • classification • clustering algorithms • Computer systems • computer vision • Databases • Data Mining • Decision Theory • Education • Fuzzy Sets • Image Processing • Information Retrieval • information systems • learning • machine learning • Rough Set Theory • Semantics • Signal Processing |
| ISBN-10 | 3-031-50958-7 / 3031509587 |
| ISBN-13 | 978-3-031-50958-2 / 9783031509582 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
