Intelligent Data Engineering and Automated Learning – IDEAL 2020

1. Front Matter

   Pages i-xxv

   PDF

2. Special Session on Data Generation and Data Pre-processing in Machine Learning

   1. Front Matter

      Pages 1-1

      PDF

   2. A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theory

      • Fares Grina, Zied Elouedi, Eric Lefevre

      Pages 3-11

   3. Multi-agent Based Manifold Denoising

      • Mohammad Mohammadi, Kerstin Bunte

      Pages 12-24

   4. A Novel Evaluation Metric for Synthetic Data Generation

      • Andrea Galloni, Imre Lendák, Tomáš Horváth

      Pages 25-34

   5. Data Pre-processing and Data Generation in the Student Flow Case Study

      • Luís Cavique, Paulo Pombinho, Antonio J. Tallón-Ballesteros, Luís Correia

      Pages 35-43

   6. Enhanced Credit Prediction Using Artificial Data

      • Peter Mitic, James Cooper

      Pages 44-53

   7. Data Generation Using Gene Expression Generator

      • Zakarya Farou, Noureddine Mouhoub, Tomáš Horváth

      Pages 54-65

   8. Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm Selection

      • Ilya Sahipov, Alexey Zabashta, Andrey Filchenkov

      Pages 66-75

3. Special Session on Optimization and Machine Learning for Industry 4.0

   1. Front Matter

      Pages 77-77

      PDF

   2. Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search

      • Eneko Osaba, Javier Del Ser, Xabier Jubeto, Andrés Iglesias, Iztok Fister Jr., Akemi Gálvez et al.

      Pages 79-91

   3. An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments

      • Izaskun Mendia, Sergio Gil-Lopez, Javier Del Ser, Iñaki Grau, Adelaida Lejarazu, Erik Maqueda et al.

      Pages 92-103

   4. Unified Performance Measure for Binary Classification Problems

      • Ana R. Redondo, Jorge Navarro, Rubén R. Fernández, Isaac Martín de Diego, Javier M. Moguerza, Juan José Fernández-Muñoz

      Pages 104-112

   5. Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks

      • Patxi Ortego, Alberto Diez-Olivan, Javier Del Ser, Basilio Sierra

      Pages 113-122

   6. A Comparison of Evolutionary Multi-objective Optimization Algorithms Applied to Antenna Design

      • Pedro B. Santos, Marcello C. Melo, Everaldo Faustino Jr., Arismar Cerqueira S. Jr., Carmelo J. A. Bastos-Filho

      Pages 123-134

4. Special Session on Practical Applications of Deep Learning

   1. Front Matter

      Pages 135-135

      PDF

   2. Cloud Type Identification Using Data Fusion and Ensemble Learning

      • Javier Huertas-Tato, Alejandro Martín, David Camacho

      Pages 137-147

   3. Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

      • Néstor Jiménez-Campfens, Adrián Colomer, Javier Núñez, Juan M. Mogollón, Antonio L. Rodríguez, Valery Naranjo

      Pages 148-155

   4. Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circumpapillary OCT Images

      • Gabriel García, Adrián Colomer, Valery Naranjo

      Pages 156-164

   5. Video Semantics Quality Assessment Using Deep Learning

      • Rui Jesus, Bárbara Silveira, Nuno Correia

      Pages 165-172

This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.*

The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

* The conference was held virtually due to the COVID-19 pandemic.

• artificial intelligence
• computer hardware
• computer networks
• computer systems
• computer vision
• data mining
• databases
• deep learning
• image processing
• information retrieval
• learning
• machine learning
• neural networks
• pattern recognition
• signal processing
• telecommunication systems

• University of Minho, Braga, Portugal

  Cesar Analide, Paulo Novais

• Technical University of Madrid, Madrid, Spain

  David Camacho

• University of Manchester, Manchester, UK

  Hujun Yin

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