Inferring Causal Gene Regulatory Networks Using Time-Delay Association Rules

Ahmed, Syed Sazzad; Roy, Swarup; Choudhury, Pabitra Pal

doi:10.1007/978-981-13-3143-5_26

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Rule-based algorithms have widely been applied to different practical contexts, such as software defect prediction [123], inferring causal gene regulatory networks [124], evaluating the efficiency of currency portfolios [125], ranking of text documents [126], relationship between student engagement and performance in E-learning environment [127], assessing web sites quality [128], and exploring shipping accident contributory factors [129]; among others.…”

Section: White-box Approachmentioning

confidence: 99%

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

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Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a stateof-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and blackbox, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications. INDEX TERMS Black-box, white-box, explainable artificial intelligence, deep learning.

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