Design Knowledge Reduction Approach Based on Rough Sets of HCI

2013 ◽  
pp. 267-277
Author(s):  
Qing Xue ◽  
Qi-qi Yin ◽  
Li-ying Feng ◽  
Min-xia Liu
Keyword(s):  
Rough Sets ◽  
Design Knowledge ◽  
Knowledge Reduction ◽  
Reduction Approach

scholarly journals Mechanical Product Ecological Design Knowledge Reduction Based on Rough Set

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 33-38 ◽  
Author(s):  
Lei Zhang ◽  
Zhifeng Jin ◽  
Yu Zheng ◽  
Rui Jiang
Keyword(s):  
Rough Set ◽  
Design Knowledge ◽  
Ecological Design ◽  
Knowledge Reduction

Maintaining Dynamic Information Systems Using Incremental Dominance-Based Rough Set Approach

2014 ◽  
Vol 631-632 ◽  
pp. 53-56
Author(s):  
Yan Li ◽  
Xiao Qing Liu ◽  
Jia Jia Hou
Keyword(s):  
Decision Making ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Rough Sets ◽  
Dynamic Information ◽  
Knowledge Reduction ◽  
Incremental Approach

Dominance-based rough sets approach (DRSA) is an effective tool to deal with information with preference-ordered attribute domain. In practice, many information systems may evolve when attribute values are changed. Updating set approximations for these dynamic information systems is a necessary step for further knowledge reduction and decision making in DRSA. The purpose of this paper is to present an incremental approach when the information system alters dynamically with the change of condition attribute values. The updating rules are given with proofs, and the experimental evaluations on UCI data show that the incremental approach outperforms the original non-incremental one.

Two-stage dimensionality reduction approach based on 2DLDA and fuzzy rough sets technique

2011 ◽  
Vol 74 (17) ◽  
pp. 3722-3727 ◽  
Author(s):  
Hao-Xin Zhao ◽  
Hong-Jie Xing ◽  
Xi-Zhao Wang
Keyword(s):  
Dimensionality Reduction ◽  
Rough Sets ◽  
Fuzzy Rough Sets ◽  
Two Stage ◽  
Reduction Approach

Personalized Product Design Knowledge Acquisition Based on Knowledge Reduction and Knowledge Mining

2015 ◽  
Vol 738-739 ◽  
pp. 275-280
Author(s):  
Er Tian Hua ◽  
Da Qiang Chen ◽  
Xiao Juan Gong ◽  
Lei Hu ◽  
Yan Zhen He ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Product Design ◽  
Customer Segmentation ◽  
Decision Table ◽  
Design Knowledge ◽  
Knowledge Mining ◽  
Knowledge Reduction ◽  
Design Information ◽  
Customer Group ◽  
Decision Variables

This paper makes the historical data which customers bought the products as the foundation, and suggests a method of personalized product design knowledge acquisition based on knowledge reduction and knowledge mining. Firstly, the core matrices of each specific customer group were taken as the decision variables of the decision table based on the key customer segmentation. Secondly, the knowledge reduction algorithm of Skowron discernibility matrix was use to acquire the product design information by reducing the decision table, and deleting the redundant and even product matrices that are not necessary. Thirdly, the product design information was taken as the initial data of knowledge mining, and the product design knowledge of specific customer group was obtained by a classification consistency algorithm. Finally, the stroller experimental design was taken as a case study by online interaction based on Web technology, and the conclusion of the study showed that the method was feasible.

scholarly journals Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

2018 ◽  
Vol 30 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Xiaoxia Xiong ◽  
Long Chen ◽  
Jun Liang
Keyword(s):  
Bayesian Networks ◽  
Rough Sets ◽  
Traffic Accidents ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Traffic Density ◽  
Secondary Tasks ◽  
Knowledge Reduction ◽  
Naturalistic Driving ◽  
Insight Into

The paper integrates Rough Sets (RS) and Bayesian Networks (BN) for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.

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