Multi-Objective Machine Learning Gebunden

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 syst ems. 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.