Overview
- Proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable
- Provides the main idea of the explainable recommenders outlined within the background of neuro-fuzzy systems
- Declares various novel recommenders, 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
- Develops an original concept of the explainable recommender, based on patterns from previous transactions
- Recommends stocks that fit the strategy of investors and its recommendations are explainable for investment advisers
Part of the book series: Studies in Computational Intelligence (SCI, volume 964)
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Table of contents (5 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
Authors: Tom Rutkowski
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-75521-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-75520-1Published: 08 June 2021
Softcover ISBN: 978-3-030-75523-2Published: 09 June 2022
eBook ISBN: 978-3-030-75521-8Published: 07 June 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIX, 167
Number of Illustrations: 46 b/w illustrations, 72 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Artificial Intelligence, Applications of Mathematics