An incremental attribute reduction method for dynamic data mining

Jing, Yunge; Li, Tianrui; Fujita, Hamido; Wang, Baoli; Cheng, Ni

doi:10.1016/j.ins.2018.07.001

Cited by 57 publications

(14 citation statements)

References 39 publications

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“…The entropy weight method is an objective weighting method based on normalization matrix calculation and is not suitable for discrete data [68,69]. The entropy weight method analyzes the influence of indicator variation on the weight [70,71], while the attribute reduction set method examines the dependence of decision attributes on conditional attributes [72][73][74]. By combining the weights obtained by both methods, this paper has comprehensively considered the importance of each attribute to decision-making and the influence of information quantity within each attribute on decision-making, thus determining the weight of attributes based on two aspects and making up for the shortcomings of the attribute reduction set method in weight determination.…”

Section: Evaluation Stepsmentioning

confidence: 99%

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Sun

Yuan

et al. 2019

Sustainability

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In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.

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Section: Evaluation Stepsmentioning

confidence: 99%

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Sun

Yuan

et al. 2019

Sustainability

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“…Attribute reduction is one of the core concepts of RS theory, which addresses incompleteness, redundancy, and ambiguity in data in the field of machine learning. This approach avoids the use of complex discernibility matrices and uses attribute importance as heuristic information to obtain inductive sets and importance analysis results; excellent results can be obtained in factor selection for prediction models based on RS theory [19][20][21]. Moreover, long shortterm memory (LSTM) based on the memory architecture in deep learning (DL) can overcome the memory shortage and vanishing gradient issues of recurrent neural networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

Yang

Chang³

2019

Journal of Sensors

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Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

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“…Attribute reduction in rough set theory has been recognized as an important feature selection method, aimed to select the most representative attribute subset with a high resolution by eliminating redundant and unimportant attributes [ 1 ]. The attribute reduction methods can be widely implemented in the fields of data classification, data mining, machine learning, and pattern recognition [ 2 , 3 , 4 , 5 , 6 ]. Due to the development of the internet, the scale of data becomes bigger and bigger.…”

Section: Introductionmentioning

confidence: 99%

“…Even thousands of attributes may be acquired in some real-world databases. In order to shorten the processing time and obtain better generalization, the attribute reduction problem attracts more and more attention in recent years [ 5 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Sun

Zhang

et al. 2019

Entropy

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Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

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An incremental attribute reduction method for dynamic data mining

Cited by 57 publications

References 39 publications

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

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