Scalable Fuzzy Rough Set Reduct Computation Using Fuzzy Min–Max Neural Network Preprocessing

Kumar, Anil; Prasad, P. S. V. S. Sai

doi:10.1109/tfuzz.2020.2965899

Cited by 21 publications

(2 citation statements)

References 34 publications

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“…It originated from Zdzislaw I. Pawlak [ 1 ] and has been identified as a creative and innovative mathematical tool in the last two decades. The rough-set-based data mining approaches have superiority in that they need no prior information, in contrast with other widely utilized strategies, such as SVM, PCA, and DNN [ 2 , 3 , 4 , 5 , 6 ]. Attribute reduction, or feature selection, has become one of the hot spots in the research area of big data.…”

Section: Introductionmentioning

confidence: 99%

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

Yan

Han

Zhang

et al. 2022

Sensors

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Due to the explosive growth of data collected by various sensors, it has become a difficult problem determining how to conduct feature selection more efficiently. To address this problem, we offer a fresh insight into rough set theory from the perspective of a positive approximation set. It is found that a granularity domain can be used to characterize the target knowledge, because of its form of a covering with respect to a tolerance relation. On the basis of this fact, a novel heuristic approach ARIPA is proposed to accelerate representative reduction algorithms for incomplete decision table. As a result, ARIPA in classical rough set model and ARIPA-IVPR in variable precision rough set model are realized respectively. Moreover, ARIPA is adopted to improve the computational efficiency of two existing state-of-the-art reduction algorithms. To demonstrate the effectiveness of the improved algorithms, a variety of experiments utilizing four UCI incomplete data sets are conducted. The performances of improved algorithms are compared with those of original ones as well. Numerical experiments justify that our accelerating approach enhances the existing algorithms to accomplish the reduction task more quickly. In some cases, they fulfill attribute reduction even more stably than the original algorithms do.

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Section: Introductionmentioning

confidence: 99%

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

Yan

Han

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…A state division method based on the consumption state of new energy is proposed. The reduction algorithm (Kumar and Prasad, 2020) is used to deal with uncertain information and massive data. Meanwhile, considering the actual situation of each consumption state and the characteristics of each optimizing adjustment equipment, a hydrogen heat energy system (HHES) including electric-hydrogen conversion, hydrogen storage, heat storage, microturbine cogeneration, and electric boiler is used in the EHHS under different consumption states to optimize and adjust.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimal Operation of Electricity Heat Hydrogen System Based on Evaluation of New Energy Consumption Potential

2021

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Under the background of the “double high” power system, the electricity heat hydrogen system (EHHS) plays a significant role in the process of energy decarbonization. In order to meet the different optimization objectives of the system under different new energy consumption states, a new energy consumption potential assessment and optimized operation method based on intuitionistic fuzzy rough set theory is proposed. By using the intuitionistic fuzzy rough set theory, the continuous attribute data is divided into different levels and the results of its membership and non-membership are gotten at different levels. The membership results of real-time consumption data are matched with the rule sets, and then the system consumption state judgment result is obtained. In this article, the system consumption situation is divided into five states, and compared with the traditional division method, so the system state can be described more comprehensively. At the same time, the fuzzy set is used to deal with the ambiguity of the boundary between each state. The intuition theory is used to solve the problem of the uncertainty of the consumption state, and then the accurate judgment can be realized. In response to different consumption states, an optimal scheduling model is established in which a hydrogen heat energy system (HHES) is involved to meet different requirements, and a hybrid particle swarm optimization algorithm is used to solve the model. Adopting the IEEE-30 bus system as the network structure of EHHS in the simulation, the analysis shows that the dynamic state division method based on intuitionistic fuzzy rough set theory can better be used to judge the system state according to real-time variable factors. The system optimization based on the consumption state division has the advantages of improving the operating economy and increasing the consumption of new energy.

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Rough Sets for Intelligence on Embedded Systems

Nesterenko

Lewis

2022

Lecture Notes in Computer Science

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Scalable Fuzzy Rough Set Reduct Computation Using Fuzzy Min–Max Neural Network Preprocessing

Cited by 21 publications

References 34 publications

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

Research on Optimal Operation of Electricity Heat Hydrogen System Based on Evaluation of New Energy Consumption Potential

Rough Sets for Intelligence on Embedded Systems

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