Explainable Artificial Intelligence Based on Neuro-fuzzy Modeling With Applications in Finance: 964 - Rilegato

Rutkowski, Tom

Rilegato

ISBN 10: 3030755207 ISBN 13: 9783030755201

Casa editrice: Springer Nature, 2021

Riassunto

<p></p><p>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.</p><br><p></p>

Su questo libro

Dalla quarta di copertina

<p>The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable.&nbsp;The vast majority of AI models work like black&nbsp;box models. However, in many applications, e.g.,&nbsp;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.&nbsp;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,&nbsp;and its recommendations are explainable for investment advisers.<br></p>

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2021
• ISBN 10: 3030755207
• ISBN 13: 9783030755201
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 188
• Contatto del produttore: non disponibile