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Explainable artificial intelligence ...

Rutkowski, Tom.

Explainable artificial intelligence based on neuro-fuzzy modeling with applications in finance

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable artificial intelligence based on neuro-fuzzy modeling with applications in financeby Tom Rutkowski.
Author:	Rutkowski, Tom.
Published:	Cham :Springer International Publishing :2021.
Description:	xix, 167 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceFinancial applications.
Online resource:	https://doi.org/10.1007/978-3-030-75521-8
ISBN:	9783030755218$q(electronic bk.)

Explainable artificial intelligence based on neuro-fuzzy modeling with applications in finance
Rutkowski, Tom.

Explainable artificial intelligence based on neuro-fuzzy modeling with applications in finance[electronic resource] /by Tom Rutkowski. - Cham :Springer International Publishing :2021. - xix, 167 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.9641860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction -- Neuro-Fuzzy Approach and its Application in Recommender Systems -- Novel Explainable Recommenders Based on Neuro-Fuzzy -- Explainable Recommender for Investment Advisers -- Summary and Final Remarks.

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.

ISBN: 9783030755218$q(electronic bk.)

Standard No.: 10.1007/978-3-030-75521-8doiSubjects--Topical Terms:

890884
Artificial intelligence
--Financial applications.

LC Class. No.: HG4515.5 / .R885 2021

Dewey Class. No.: 332.640285

Explainable artificial intelligence based on neuro-fuzzy modeling with applications in finance
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