Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

Tom Rutkowski

ISBN 10: 3030755207 / ISBN 13: 9783030755201

Published by Springer International Publishing, 2021

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030755201

Bibliographic Details

• Title: Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
• Publisher: Springer International Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 3030755207
• ISBN 13: 9783030755201
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 16D millimeters 241 millimeters width by 160 millimeters height by 16 millimeters depth
• Weight: 453 grams

About this title

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.