Attribute reduction in decision-theoretic rough set models

Yao, Yiyu; Zhao, Yan

doi:10.1016/j.ins.2008.05.010

Cited by 503 publications

(197 citation statements)

References 42 publications

(62 reference statements)

Supporting

Mentioning

197

Contrasting

Order By: Relevance

“…But ANN cannot deal with qualitative factors directly, so the complex system should be simplified to a quantitative system. In this study, the rough set theory method based on attribute reduction rule (ARR) [28][29] is introduced to meet the above demands. The algorithm of ARR is described simply as fig 1. The results of ARR are shown in Table 1, indicating that CCS and slump are influenced mainly by the mix proportion, namely the consumptions of the raw materials is made of the equivalent subset.…”

Section: B the Algorithm Of Ga-bpmentioning

confidence: 99%

Predictions of concrete compressive strength based a hybrid algorithm of GA-BP

Shi¹,

Zhong²,

Zhang³

et al. 2018

Proceedings of the 4th Annual International Conference on Material Engineering and Application (ICMEA 2017)

View full text Add to dashboard Cite

Abstract-The concrete quality is of great significance for the safety of buildings. This paper conducted the research on this subject in the actual concrete mixing plant, with concrete compressive strength (CCS) being the indicator of concrete quality. Firstly, in order to make the quality evaluation models feasible, the rough set theory method based attribute reduction rule (ARR) was introduced to build the equivalent quality evaluation models with fewer measurable factors. Then, along with the consumptions of raw materials, the correlation coefficients of the consumption amounts, including sand ratio, water-cement ratio, and water-binder ratio,were determined as the input parameters of GA-BP. The prediction accuracy, stability and reliability (RMSE:1.39; : 3.20%; S:2.18%) were better than those of the contrast schemes. Furtherly, the research shew that the rules of some factors, such as personnel, material properties, equipment status, and environment, are step functions of time, so the time threshold was introduced into GA-BP models to reduce the influence of these factors on the model, the algorithm of determining the optimal time threshold was given. In the case study, the appropriate time threshold improved the accuracy of GA-BP. Finally, the proposed models and the algorithms were applied in the actual plant operations to estimate CCS on line, the satisfied results were achieved.

show abstract

Section: B the Algorithm Of Ga-bpmentioning

confidence: 99%

Predictions of concrete compressive strength based a hybrid algorithm of GA-BP

Shi¹,

Zhong²,

Zhang³

et al. 2018

Proceedings of the 4th Annual International Conference on Material Engineering and Application (ICMEA 2017)

View full text Add to dashboard Cite

show abstract

“…A rough set methodology is based on the premise that lowering the degree of precision in the data makes the data pattern more visible [12], whereas the central premise of the rough set philosophy is that the knowledge consists in the ability of classification. In other words, the rough set approach can be considered as a formal framework for discovering facts from imperfect data [3].…”

Section: Basic Concept Of Rough Set Theory:-mentioning

confidence: 99%

A Rough Set Based Classification Model for the Generation of Decision Rules

Rampure¹,

Tiwari²

2014

IJDTA

View full text Add to dashboard Cite

show abstract

“…The theory of Rough Sets introduces the concept of Reduct [7] to address this issue. Reduct is a process than results in a set of attributes which are "jointly sufficient" [7] and "individually necessary" [7] for evaluating the values of a specific attribute. For instance, suppose we want to find the positive region of -Has Flue = Yes‖.…”

Section: Reductmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

View full text Add to dashboard Cite

Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

show abstract

Attribute reduction in decision-theoretic rough set models

Cited by 503 publications

References 42 publications

Predictions of concrete compressive strength based a hybrid algorithm of GA-BP

Predictions of concrete compressive strength based a hybrid algorithm of GA-BP

A Rough Set Based Classification Model for the Generation of Decision Rules

A Rough Sets-based Agent Trust Management Framework

Contact Info

Product

Resources

About