This paper gives an overview of meta-heuristics methods utilized within the paradigm of
multi-objective programming. This is an area of research that has undergone substantial …

This paper reviews the application of multiobjective optimization in the fields of
bioinformatics and computational biology. A survey of existing work, organized by …

Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

Importance The development of artificial intelligence (AI) and other machine diagnostic
systems, also known as software as a medical device, and its recent introduction into clinical …

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 …

This authoritative handbook reveals the connections between bioinspired techniques and
the development of solutions to problems that arise in diverse problem domains. It provides …

One of the major challenges in medical domain is the extraction of comprehensible
knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary …

It is well understood that the optimal classification decision variable is the likelihood ratio or
any monotonic transformation of the likelihood ratio. An automated classifier which maps …

The receiver operating characteristic (ROC) has become a standard tool for the analysis and
comparison of classifiers when the costs of misclassification are unknown. There has been …

Ensemble learning is a system that improves the performance and robustness of the
classification problems. How to combine the outputs of base classifiers is one of the …