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Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
List of contents
Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
Summary
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
