Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance, 1st ed. 2021 Studies in Computational Intelligence Series, Vol. 964

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.

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