Multiobjective Machine Learning 1st Edition Mohua Banerjee Sushmita Mitra by Mohua Banerjee, Sushmita Mitra, Ashish Anand (auth.), Yaochu Jin Dr. (eds.) 9783540306764, 9783540330196, 3540306765, 3540330194 instant download after payment.

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.