Fast Causal Division for Supporting High Dimensional Causal Discovery

Mai, Guizhen; Peng, Shiguo; Hong, Yinghan; Chen, Pinghua

doi:10.1109/cse-euc.2017.59

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hybrid method is just based on the combination of constraint method and causal function model. Cai et al [28] proposed SADA framework. This method adopts the strategy of splitting and merging, and uses the causal network of local sparsity structure, which can accurately determine the causal variables in the case of high dimension and low sample.…”

Section: B Causal Reasoningmentioning

confidence: 99%

Operation Mode Decision of Indoor Cleaning Robot Based on Causal Reasoning and Attribute Learning

Zhang

Yin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

At present, the cleaning robots on market generally have the defects of simple operation mode and weak intelligence. In order to improve the intelligent degree and operation ability of cleaning robots, this paper proposes a decision method for cleaning robot's operation mode. Firstly, use the hierarchical expression ability of deep network to obtain the attributes of garbage such as state, shape, distribution, size and so on. Then the causal relationship between the attributes and the operation modes can be built by using joint learning of association attributes with depth network model and causal inference. Based on this, a fuzzy inference network for operation mode decision is designed. With the help of causal analysis, the structure of the decision model is greatly simplified. Compared with conventional fuzzy neural networks, the total parameters of the model are reduced by 2 / 3. The method proposed in this paper imitates the way that human dispose of different types of garbage and has good interpretability. The experimental results verify the effectiveness of the proposed method. INDEX TERMS attributes learning, causal reasoning, cleaning robot, fuzzy inference network, joint learning.

show abstract

Section: B Causal Reasoningmentioning

confidence: 99%