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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance/ by Tom Rutkowski.
Author:	Rutkowski, Tom.
Description:	XIX, 167 p. 118 illus., 72 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-75521-8
ISBN:	9783030755218

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. - 1st ed. 2021. - XIX, 167 p. 118 illus., 72 illus. in color.online resource. - Studies in Computational Intelligence,9641860-9503 ;. - Studies in Computational Intelligence,564.

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

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

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

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