Neural networks for classification: a survey

TLDR

Neural‑network classification is a highly active research area with a rapidly expanding literature. The paper aims to summarize key developments in neural‑network classification and stimulate further research in identified topics. The survey examines posterior probability estimation, the relationship between neural and conventional classifiers, learning–generalization tradeoffs, feature selection, and misclassification cost effects.

Abstract

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.