Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance (Studies in Computational Intelligence, 964)

Rutkowski, Tom

ISBN 10: 3030755207 / ISBN 13: 9783030755201

Edité par Springer, 2021

A propos de cet article

N° de réf. du vendeur ABLIING23Mar3113020028985

Détails bibliographiques

Titre : Explainable Artificial Intelligence Based on...

Éditeur : Springer

Date d'édition : 2021

Reliure : Couverture rigide

Etat : New

A propos de ce titre

Synopsis :

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.