Artificial Intelligence and Soft Computing

Name: Artificial Intelligence and Soft Computing | 17th International Conference, ICAISC 2018, Zakopane, Poland, June 3-7, 2018, Proceedings, Part I
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17th International Conference, ICAISC 2018, Zakopane, Poland, June 3-7, 2018, Proceedings, Part I

Leszek Rutkowski Rafal Scherer Marcin Korytkowski Witold Pedrycz Ryszard Tadeusiewicz Jacek M. Zurada(Editor)

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Published on 24. May 2018

XXV, 774 pages

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Content

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Neural Networks and Their Applications
Three-Dimensional Model of Signal Processing in the Presynaptic Bouton of the Neuron
1 Introduction
2 Motivations
3 The Model
4 Simulations
5 Results
6 Concluding Remarks
References
The Parallel Modification to the Levenberg-Marquardt Algorithm
1 Introduction
2 The Classic Levenbert-Marquardt Algorithm
3 Parallel Modification
4 Results
4.1 Approximating y = 4x(1 - x) Function
4.2 Approximating y = sinx logx Function
5 Conclusion
References
On the Global Convergence of the Parzen-Based Generalized Regression Neural Networks Applied to Streaming Data
1 Introduction
2 Algorithm
3 Global Convergence of Algorithm (4)
4 Experimental Results
5 Conclusions
References
Modelling Speaker Variability Using Covariance Learning
1 Introduction
2 Model Formulation
3 Model Implementation
3.1 PCA Implementation
3.2 SOM Implementation
4 Covariance Learning
5 Results
5.1 Overall Average Analysis
5.2 Principal Component Visualisation
5.3 SOM Covariance Visualisation
6 Conclusion
References
A Neural Network Model with Bidirectional Whitening
1 Introduction
2 Multilayer Perceptron and Natural Gradient Descent
2.1 Multilayer Perceptron
2.2 Natural Gradient Method
2.3 Approximation of the Fisher Information Matrix
3 Whitened Neural Networks
3.1 Natural Gradient Descent by Whitening
3.2 Extension of Whitening
4 Numerical Experiment
5 Discussion
References
Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle
Abstract
1 Introduction
2 Method
2.1 Video Camera Calibration
2.2 Block Match Algorithm
2.3 Semi-Global Block Match (SGBM) Algorithm
3 Design of UAV with Obstacle Avoidance System
3.1 Hardware
3.2 Software
4 Experimental Validation of Obstacle Avoidance System
4.1 Experimental Setting
4.2 Results
5 Conclusion
Acknowledgements
References
Prototype-Based Kernels for Extreme Learning Machines and Radial Basis Function Networks
1 Introduction
2 Prototype-Based Kernels for Extreme Learning Machines
3 Comparison of New Algorithm with Others
4 Summary
References
Supervised Neural Network Learning with an Environment Adapted Supervision Based on Motivation Learning Factors
1 Introduction
2 Limitations of the Supervised Learning Algorithms
3 Environment Based Motivated Learning Factors
4 Environment Adapted Supervision of Neural Network
5 Experiments Applied to a Robot in a Certain Environment
6 Adaptive Knowledge-Based Learning
7 Conclusions and Remarks
References
Autoassociative Signature Authentication Based on Recurrent Neural Network
1 Introduction
2 Autoassociative Recurrent Neural Network Authentication (ARNN)
2.1 Normalization and Feature Extraction
2.2 Reference Modeling
3 Experimental Results
3.1 Experimental Conditions
3.2 Authentication Experiment
4 Conclusion
References
American Sign Language Fingerspelling Recognition Using Wide Residual Networks
1 Introduction
2 Related Work
3 Problem Description
4 Applied Deep Learning Techniques
5 Solution Description
6 Experiments
7 Conclusions and Future Work
References
Neural Networks Saturation Reduction
1 Introduction
2 Neural Network Saturation
2.1 Neuron Saturation Detection
2.2 Changing Working Space
3 Saturation Reduction - Example
4 Experimental Results
5 Conclusions
References
Learning and Convergence of the Normalized Radial Basis Functions Networks
1 Introduction
2 Nonlinear Function Learning
3 Recursive Classification Rules
4 Consistency and Rates of Convergence
4.1 Convergence Results
4.2 Outlines of Proofs
5 Conclusions
References
Porous Silica-Based Optoelectronic Elements as Interconnection Weights in Molecular Neural Networks
1 Introduction
2 Experimental Part
3 Conclusion
References
Data Dependent Adaptive Prediction and Classification of Video Sequences
1 Introduction
2 Description of the APCN Architecture
3 Algorithm
3.1 Data-Dependent Pooling
3.2 Cost Function
4 Simulation Setup and Results
4.1 Synthetic Data Set Generation
4.2 Prediction Using Data Dependent Pooling
4.3 Adaptability of the Network
5 Conclusion
References
Multi-step Time Series Forecasting of Electric Load Using Machine Learning Models
1 Introduction
2 Time Series Forecasting Strategies
2.1 Single-Step Forecasting
2.2 Multi-step Forecasting
3 ARIMA Based Time Series Forecasting
4 LSTM Based Time Series Forecasting
5 Experiments and Results
5.1 Dataset
5.2 ARIMA Model Formulation
5.3 LSTM Model Formulation
5.4 Forecast Performance of the Models
5.5 Discussion
6 Conclusions
References
Deep Q-Network Using Reward Distribution
1 Introduction
2 Deep Q-Network
2.1 Structure
2.2 Learning
3 Deep Q-Network Using Reward Distribution
4 Computer Experiment Results
4.1 Experimental Conditions
4.2 Score Transition
4.3 Transition of Acquired Negative Reward
5 Conclusion
References
Motivated Reinforcement Learning Using Self-Developed Knowledge in Autonomous Cognitive Agent
1 Introduction
2 Robot's Reinforcement Learning Using Motivation
2.1 Interaction with the Surroundings
2.2 External Rewards and Reinforcement Learning
2.3 Internal Rewards and Motivation Factors
2.4 Motivation to Achieve the Goal
2.5 Deep Q-Learning
2.6 Motivation to Explore and Curiosity
2.7 Experience Replay
2.8 Final Algorithm Controlling the Robot's Movements
3 Simulation and Experimental Results
3.1 Virtual Environment
3.2 Robot Rating
3.3 Learning Rate
3.4 Neural Network Structure
3.5 Motivation to Explore
4 Conclusions and Final Remarks
References
Company Bankruptcy Prediction with Neural Networks
1 Introduction
2 Methodology of the Research
3 Experimental Results
4 Conclusion
References
Soft Patterns Reduction for RBF Network Performance Improvement
1 Introduction
2 Concept of Soft Patterns Reduction
2.1 Error Correction Algorithm
3 Experimantal Results
3.1 Peaks Function
3.2 Schwefel Function
3.3 Shaffer Function
4 Conclusions
References
An Embedded Classifier for Mobile Robot Localization Using Support Vector Machines and Gray-Level Co-occurrence Matrix
1 Introduction
2 Related Works
Mobile Robots Localization and Navigation.
Embedded SVM Classifiers.
Embedded GLCM Feature Extractor.
3 Proposed Approach
3.1 Data Base
3.2 Feature Extractor and the Pattern Recognition
3.3 Feature Selection and Reduction
Feature Selection Based on Correlation (CFS).
Study on Features Complexity.
3.4 Selecting the Input Image Size
3.5 Memory Consumption
Analytical Study.
4 Classifier Evaluation on an Embedded Platform
5 Conclusions
References
A New Method for Learning RBF Networks by Utilizing Singular Regions
1 Introduction
2 Background
2.1 RBF Networks
2.2 Existing Methods for Learning RBF Networks
2.3 Singular Regions of RBF Networks
3 SSF for RBF Networks
3.1 General Flow of RBF-SSF
3.2 Techniques for Making SSF Faster
4 Experiments
4.1 Experiments Using Engine Behavior Dataset
4.2 Experiment Using Building Energy Dataset
4.3 Experiment Using Parkinsons Telemonitoring Dataset
4.4 Findings Obtained from Experiments
5 Conclusion
References
Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization
1 Introduction
2 Methodology
2.1 FPGA Implementation of Cyclic Reservoir Systems
2.2 Nonlinear Channel Equalization
3 Results
4 Conclusions
References
Discrete Cosine Transform Spectral Pooling Layers for Convolutional Neural Networks
1 Introduction
2 Spectral Representation
2.1 Spectral Convolution
2.2 Spectral Pooling
3 Discrete Cosine Transform Spectral Pooling
4 Experiment Results
5 Final Remarks
References
Extreme Value Model for Volatility Measure in Machine Learning Ensemble
Abstract
1 Introduction
2 Prediction Results Improvement
3 Volatility as Destructive Components Indicator
4 Practical Experiment
5 Conclusions
References
Deep Networks with RBF Layers to Prevent Adversarial Examples
1 Introduction
2 Adversarial Examples and Related Work
3 Deep Networks with RBF Layers
4 Experimental Results
5 Conclusion
References
Application of Reinforcement Learning to Stacked Autoencoder Deep Network Architecture Optimization
1 Introduction
2 Stacked Autoencoder Deep Network
3 Reinforcement Learning
3.1 Introduction
3.2 Q(0)-learning
4 Proposed Approach
5 Empirical Results and Discussion
6 Conclusions
References
Evolutionary Algorithms and Their Applications
An Optimization Algorithm Based on Multi-Dynamic Schema of Chromosomes
1 Introduction
2 Methodology
2.1 Dissimilarity Operator
2.2 Similarity Operator
2.3 Dynamic Schema Operator
2.4 Dynamic Dissimilarity Operator
3 The MDSDSC Algorithm
4 Experimental Results
5 Conclusion
References
Eight Bio-inspired Algorithms Evaluated for Solving Optimization Problems
1 Introduction
2 Bio-inspired Algorithms
3 Automatic Parameter Tuning
4 Optimization Problems
5 Performance Analysis
6 Statistical Analysis
7 Convergence Analysis
8 Conclusion
References
Robotic Flow Shop Scheduling with Parallel Machines and No-Wait Constraints in an Aluminium Anodising Plant with the CMAES Algorithm
1 Introduction
2 Robotic Flow Shop Problems
3 CMAES for Robotic Flow Shop Scheduling
3.1 Background on CMAES
3.2 The CMAES-Based Robotic Flow Shop Scheduling Algorithm
4 Empirical Evaluation
5 Using the CMAES Algorithm for Business Decisions
6 Conclusion
References
Migration Model of Adaptive Differential Evolution Applied to Real-World Problems
1 Introduction
2 Adaptive Variants of Differential Evolution
3 Migration Model of DE
4 Experimental Settings
5 Results and Discussion
6 Conclusion
References
Comparative Analysis Between Particle Swarm Optimization Algorithms Applied to Price-Based Demand Response
1 Introduction
2 Demand Response and Load Scheduling
3 Autonomous and Distributed Modelling
4 Particle Swarm Optimization
4.1 Classical and Linear Decreasing Weight PSO
4.2 Proposed PSO Based on Stochastic Population Mechanism
5 Results and Discussions
References
Visualizing the Optimization Process for Multi-objective Optimization Problems
1 Introduction
2 Background
2.1 Visualization Techniques
2.2 Low-dimensionality Visualization Techniques
3 Proposed Approach
3.1 Scatterplot
3.2 Animation
3.3 Visualizing the State of the Algorithm
4 Experimental Setup and Results
4.1 Experiment I
4.2 Experiment II
5 Conclusion
References
Comparison of Constraint Handling Approaches in Multi-objective Optimization
1 Introduction
2 Background
3 Experimental Setup
3.1 Algorithms
3.2 Performance Measures
3.3 Implementation
3.4 Procedure
4 Results
4.1 GD
4.2 IGD
4.3 HV
4.4 Spread
5 Conclusion
References
Genetic Programming for the Classification of Levels of Mammographic Density
1 Introduction
2 Previous Work
3 Methodology
3.1 Thresholding
3.2 Feature Extractions
3.3 Genetic Programming
4 Experimental Results
5 Conclusions
References
Feature Selection Using Differential Evolution for Unsupervised Image Clustering
1 Introduction
2 Theoretical Aspects
2.1 Feature Selection
2.2 Differential Evolution
2.3 Unsupervised Cluster Evaluation Metrics
3 Method
3.1 Overview
3.2 Preprocessing
3.3 Feature Extraction and Selection Setup
3.4 Clustering and Evaluation
4 Experimental Results and Analysis
4.1 Dataset Description
4.2 Evaluation Measures
4.3 Cluster Analysis
5 Conclusion
References
A Study on Solving Single Stage Batch Process Scheduling Problems with an Evolutionary Algorithm Featuring Bacterial Mutations
1 Introduction and Literature Review
2 Problem Definition
3 The Proposed Approach
3.1 Representation of Schedules and the Mutation
3.2 Fitness Function
3.3 Fine Tuning
4 Computational Results
5 Concluding Remarks
References
-1Observation of Unbounded Novelty in Evolutionary Algorithms is Unknowable
1 Artificial Life and Endless Novelty
1.1 Identifying Unlimited Novelty
2 Prefix Free Complexity and Algorithmic Information
2.1 Defining the Set Generating Program
2.2 Kolmogorov Complexity is not Computable
2.3 Chaitin's Incompleteness Theorem
3 Limits on Identifying Novelty
3.1 Definitions of Novelty
3.2 Commonalities of Novelty
3.3 Necessary Condition for Novelty
3.4 Unbounded Novelty Detection is Nonalgorithmic
4 Conclusion
References
Multi-swarm Optimization Algorithm Based on Firefly and Particle Swarm Optimization Techniques
1 Introduction
2 Particle Swarm Optimization
3 Firefly Algorithm
3.1 A Canonical Version of Firefly Algorithm
3.2 Firefly Particle Swarm Optimization
3.3 Proposed Hybrid Algorithm
4 Experimental Setup
5 Results
6 Conclusion
References
New Running Technique for the Bison Algorithm
1 Introduction
2 Bison Algorithm
2.1 Bison Algorithm Definition
Swarming Behavior.
Running Behavior.
Proposition of the New Running Behavior.
Bison Algorithm with the New Running Behavior.
Parameters and Out of Bounds Behavior.
3 Experiments and Results
4 Discussion
5 Conclusion
References
Evolutionary Design and Training of Artificial Neural Networks
1 Introduction
2 Used Methods and Algorithms
2.1 Self-Organizing Migrating Algorithm
2.2 Evolution Strategies
3 Experiment Design
3.1 Synthesis Using Analytic Programming
3.2 Synthesis by Means of Evolutionary Algorithm
3.3 Synthesis Using Network Growth Model
4 Simulations and Results
4.1 Algorithm Settings
4.2 Results
4.3 Sample Output
5 Conclusion
References
Obtaining Pareto Front in Instance Selection with Ensembles and Populations
1 Introduction
2 Bagging Ensembles of DROP5 Instance Selection Algorithms
3 NSGA-II Based Evolutionary Instance Selection
4 Experiments and Results
5 Conclusions
References
Negative Space-Based Population Initialization Algorithm (NSPIA)
1 Introduction
2 Description of Proposed Initialization Algorithm
3 Simulation Results
4 Conclusions
References
Deriving Functions for Pareto Optimal Fronts Using Genetic Programming
1 Introduction
2 Background
2.1 Approaches
2.2 Genome Representation
3 Algorithm
3.1 Initialization
3.2 One Iteration
3.3 Selection Operator
3.4 Crossover
3.5 Mutation
3.6 Stopping Conditions
3.7 Clean Up
3.8 New Individuals
3.9 Fitness Calculation
4 Summation Operator
4.1 Start and End Value
4.2 Counter Variable
4.3 Value Calculation
5 Results
6 Conclusion and Future Work
References
Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms
Abstract
1 Introduction
2 Literature Review
3 Methodology
3.1 Feature Extraction
3.2 Machine Learning Classification Methods
3.3 Genetic Algorithm
4 Experimental Results
4.1 Database Description
4.2 Classifiers Parameter Tuning
4.3 Classification Results
5 Conclusions and Future Work
Acknowledgements
References
Particle Swarm Optimization with Single Particle Repulsivity for Multi-modal Optimization
1 Introduction
2 Particle Swarm Optimization Algorithm
3 Proposed Modification
4 Experiment
5 Results
5.1 Convergence in Higher Dimensions
6 Results Discussion
7 Conclusion
References
Hybrid Evolutionary System to Solve Optimization Problems
1 Introduction
2 The Proposed Hybrid Evolutionary System
3 Problem Formulation
4 Computational Experiment
5 Final Remarks
References
Horizontal Gene Transfer as a Method of Increasing Variability in Genetic Algorithms
1 Introduction
2 Proposed Modification
3 Test Problems
3.1 The Ackley Function
3.2 The Rosenbrock Function
3.3 The Schwefel Function
4 Numerical Results
5 Summary
References
Evolutionary Induction of Classification Trees on Spark
1 Introduction
1.1 Spark
1.2 Related Works
2 Global Decision Tree Induction Framework
3 Spark-Accelerated Evolutionary Induction
4 Experimental Validation
5 Conclusion
References
How Unconventional Chaotic Pseudo-Random Generators Influence Population Diversity in Differential Evolution
1 Introduction
2 Motivation and Related Research
3 Chaotic Systems for CPRNGs
4 The Concept of ChaosDE with Discrete Chaotic System as Driving CPRNG
5 Differential Evolution
5.1 The jDE Algorithm
6 Experiment Design
7 Results
8 Conclusions
References
An Adaptive Individual Inertia Weight Based on Best, Worst and Individual Particle Performances for the PSO Algorithm
1 Introduction
2 Inertia Weight Mechanisms
3 Proposed Adaptive Inertia Weight Mechanism
4 Experimental Setup
5 Experimental Results and Discussions
6 Conclusions
References
A Mathematical Model and a Firefly Algorithm for an Extended Flexible Job Shop Problem with Availability Constraints
1 Introduction
2 MILP Model
3 Firefly Algorithm
3.1 Variations of Light Intensity and Attractiveness
3.2 Firefly Representation for the FJSP
3.3 Discrete Firefly Algorithm for the FJSP
3.4 Distance
3.5 Attraction and Movement
4 Numerical Results
4.1 Fattahi Instances
4.2 FJSP-FCR Instances
4.3 Brandimarte Instances
4.4 Kacem Instances
5 Conclusion
References
On the Prolonged Exploration of Distance Based Parameter Adaptation in SHADE
1 Introduction
2 From DE to Db_SHADE
2.1 SHADE
2.2 Db_SHADE
3 Experimental Settings
3.1 SHADE and Db_SHADE Settings
3.2 Cluster Analysis
4 Results and Discussion
5 Conclusion
References
Investigating the Impact of Road Roughness on Routing Performance: An Evolutionary Algorithm Approach
1 Introduction
2 Literature Review
2.1 Vehicle Routing
2.2 Vehicle Operating Costs
2.3 Solution Strategies
3 Selected Algorithms
3.1 Greedy Heuristic
3.2 Simulated Annealing
3.3 CMA-ES
4 Algorithm Evaluation
5 Conclusion
References
Pattern Classification
Integration Base Classifiers in Geometry Space by Harmonic Mean
1 Introduction
2 Basic Concept
3 Proposed Method
4 Experimental Studies
5 Conclusion
References
Similarity of Mobile Users Based on Sparse Location History
Abstract
1 Introduction
2 Sparse Location Histograms
2.1 Locations, Distance and Places
2.2 Histogram Matching
3 Experiments
3.1 Data Extraction
3.2 Test Setup and Results
4 Conclusion
References
Medoid-Shift for Noise Removal to Improve Clustering
Abstract
1 Introduction
2 Existing Work and Their Limitations
3 Medoid-Shift Noise Removal
3.1 Method Description
3.2 Mean or Median?
4 Experiments
4.1 Datasets
4.2 Noise Models
4.3 Results
5 Conclusions
References
Application of the Bag-of-Words Algorithm in Classification the Quality of Sales Leads
Abstract
1 Introduction
2 Bag-of-Words Algorithm
3 Description of the Presented Method
4 Experimental Research
5 Conclusions
References
Probabilistic Feature Selection in Machine Learning
1 Introduction
2 Methodology
2.1 Architecture
2.2 Proposed Mathematical Foundation
2.3 Algorithm
3 Validation
4 Discussion
References
Boost Multi-class sLDA Model for Text Classification
1 Introduction
2 Dimensionality Reduction for Text Classification
3 Multi-class sLDA Model
3.1 Multi-class sLDA Computation
3.2 Approximate Inference
3.3 Estimation
3.4 Prediction
4 Boost Multi-class sLDA
4.1 Ensemble
4.2 AdaBoost for Multi-class sLDA
4.3 Changes to Estimation of Parameters
5 Empirical Study
5.1 Multi-class sLDA for Various Number of Topics
5.2 Boost Multi-class sLDA for Varying Number of Topics
5.3 Boost Multi-class sLDA for Best Number of Topics
6 Summary
References
Multi-level Aggregation in Face Recognition
1 Introduction
2 Description of LBP Algorithm and Possible Stages of Data Aggregation
2.1 Aggregation of Face Features and Color Channels
3 Experimental Studies
3.1 Modification of LBP Algorithm Based on Aggregation of Data Describing Variations
3.2 Aggregation of Different Parts of the Face and Different Channels
4 Conclusions and Future Studies
References
Direct Incorporation of L1-Regularization into Generalized Matrix Learning Vector Quantization
1 Introduction
2 Related Work
3 Method
4 Results
4.1 Artificial Data
4.2 Audio Emotion Recognition
5 Conclusion
References
Classifiers for Matrix Normal Images: Derivation and Testing
1 Introduction
2 Problem Statement
3 Performance Evaluation by Monte Carlo Experiments
4 Testing MCL on Images of Flames of a Gas Burner
5 Recommendations and Conclusions
References
Random Projection for k-means Clustering
Abstract
1 Introduction
2 Projection-Based Initialization
2.1 One-Dimensional Projections
2.2 Random Projection in Higher Dimensions
2.3 Random Projection
2.4 Furthest Point Projection
2.5 Repeated K-means
3 Experiments
4 Conclusions
References
Modified Relational Mountain Clustering Method
Abstract
1 Introduction
2 Mountain Clustering Methods
2.1 Mountain Method
2.2 Modified Mountain Clustering Algorithm
3 Modified Relational Mountain Clustering Method
3.1 Relational Mountain Clustering Method
3.2 The Proposed Modified Relational Mountain Clustering Method
4 Numerical Examples and Comparisons
5 Conclusions
References
Relative Stability of Random Projection-Based Image Classification
1 Introduction
2 Random Projections
3 An Image and Its Classification
3.1 Data Used for Experiments
3.2 Stability of the Classifier with Respect to RP
4 Random Projections of the Whole Image Treated as a Vector
5 Projections of Images Partitioned into Blocks
5.1 Block-Based PCA
6 Conclusions
References
Cost Reduction in Mutation Testing with Bytecode-Level Mutants Classification
1 Introduction
2 Classification of Mutants
2.1 Mutants Generation
2.2 Classification Process
3 Experimental Results
4 Conclusions
References
Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning
1 Introduction
2 Cross-Entropy in Learning in LVQ
2.1 Standard Soft Learning Vector Quantization (SLVQ)
2.2 Cross-Entropy as an Information Theoretic Objective for the Probabilistic LVQ
3 Experiments
4 Conclusions
References
Multi-class and Cluster Evaluation Measures Based on Rényi and Tsallis Entropies and Mutual Information
1 Introduction
2 Information Theoretic Cluster/Classification Evaluation Based on Shannon Entropy
2.1 General Description of the Evaluation Approach
2.2 Numerical Realization
3 Information Theoretic Cluster/Classification Evaluation Based on Rényi Entropy
4 Information Theoretic Cluster/Classification Evaluation Based on Tsallis Entropy
5 Numerical Experiments
5.1 Artificial Illustrative Example
5.2 Real World Example
6 Conclusion
References
Verification of Results in the Acquiring Knowledge Process Based on IBL Methodology
1 Introduction
2 Methods Used to Acquire Knowledge
3 Stages of Acquiring Knowledge from an Expert
4 Dermatological Asymmetry Measures (DAS) and Dermatological Asymmetry Measure of Shape (DASMShape)
5 Research and Discussion of the Results
6 Conclusions
References
A Fuzzy Measure for Recognition of Handwritten Letter Strokes
1 Introduction
2 Defining the Problem
3 Polynomial Representation of the Stroke
3.1 Methods of Comparison
3.2 Window Normalization
3.3 Component Functions
4 Tests
4.1 Quality Measure
4.2 Results
5 Conclusions
References
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