Citation

Sin, Yin Tan and Wooi, Ping Cheah and Shing, Chiang Tan (2019) Integrating Deep Learning and Bayesian Reasoning. Communications in Computer and Information Science, 1123. pp. 119-130. ISSN 1865-0929

Text 106.pdf - Published Version Restricted to Repository staff only Download (37MB)

Abstract

Deep learning (DL) is an excellent function estimator which has amazing result on perception tasks such as visualization recognition and text recognition. But, its inner architecture acts as a black box, because the users cannot understand why such decisions are made. Bayesian reasoning (BR) provides explanation facility and causal reasoning in terms of uncertainty which is able to overcome demerit of DL. This paper is to propose a framework for the integration of DL and BR by leveraging their complementary merits based on their inherent internal architecture. The migration from deep neural network (DNN) to Bayesian network (BN) involves extracting rules from DNN and constructing an efficient BN based on the rules generated, to provide intelligent decision support with accurate recommendations and logical explanations to the users.

Item Type:	Article
Uncontrolled Keywords:	Black box of deep learning Bayesian reasoning Integration Rule extraction
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Oct 2021 01:27
Last Modified:	28 Oct 2021 01:27
URII:	http://shdl.mmu.edu.my/id/eprint/8833

Downloads

Edit (login required)