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.
