Economic and financial prediction using rough sets model

Tay, Francis Eng Hock; Shen, Lixiang

doi:10.1016/s0377-2217(01)00259-4

Cited by 171 publications

(62 citation statements)

References 21 publications

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“…In recent years, the application research of rough sets in credit risk evaluation is becoming increasingly prosperous. The research of foreign scholars such as Dimitras et al (1999) [29], Malcolm and Michael (2001) [30], Tay and Shen (2002) [31] and etc has a certain representativeness. At home, Konglin Ke (2006) [32] introduced the rough set model which has nonparametric test characteristics and adaptability to noise data to commercial bank personal credit assessment.…”

Section: Rough Setsmentioning

confidence: 99%

Review of Application Research of Support Vector Machines and Rough Sets in Credit Risk Evaluation

Li¹,

Zhang²

2017

dtssehs

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Abstract. Theoretical and empirical research on credit risk evaluation plays an important role for enterprises in reducing transaction costs, enhancing competition ability and creating a good competition environment. At present, in view of the application of support vector machines and rough sets in artificial intelligence method in enterprise credit risk evaluation, research abroad mainly focuses on the innovative application of support vector machines and rough sets; and the domestic research mainly focuses on the effectiveness, the advantages and the feasibility of support vector machines and rough sets. In this paper, the research status of support vector machines and rough sets at home and abroad is discussed in detail so that the development of artificial intelligence theory and practice is promoted.

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Section: Rough Setsmentioning

confidence: 99%

Review of Application Research of Support Vector Machines and Rough Sets in Credit Risk Evaluation

Li¹,

Zhang²

2017

dtssehs

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“…It can simplify the indices in the premise of retaining key information and obtain the minimum expression of the knowledge. Pawlak and Hampton [27][28][29][30][31] defined a 2-tuple S = (U, R) to describe the information system, where U is the universe, R is a nonempty finite set of attributes. Let r ∈ R be a property of U , [x] r be the equivalence classes on the properties of the elements of U , and P ⊆ R, P = ∅, P = {r i1 , · · · , r ik }, the intersection of all equivalence relations of P is defined by P = k j=1 r ij , then P is an equivalence relation, noted IN D(P ).…”

Section: Evaluation Systemmentioning

confidence: 99%

Evaluation of the Efficiency of Low Carbon Industrialization in Cultural and Natural Heritage: Taking Leshan as an Example

Yao

2014

Sustainability

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This paper concentrates on the evaluation of the efficiency of low carbon industrialization in the tourism sector. Combining the general indices of the regional industrialization with the specific characteristics of low carbon development in the tourism sector, a comprehensive index system is scientifically designed. Due to the complexity of the index system and the tight correlation among some indices, rough set theory (RST) is applied to reduce the dimensions of the index system and delete some overlapped information. Then, the data envelopment analysis (DEA) is used to evaluate the efficiency of low carbon industrialization in every year, in order to investigate the development of regional economy, where the year is considered as the Decision-Making Unit (DMU). Furthermore, the super efficiency value of each DMU is calculated to compare the level of low carbon industrialization in each year. Finally, Leshan city, which is a typical representative of a World Cultural and Natural region in China, is considered as a practical application to show the effectiveness of the proposed model. Then, some valuable suggestions are given to help decision makers improve regional development.

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“…Several models based on original rough set theory have been presented to solve different problems [7][8]. It has important applications to artificial intelligence and economic and financial prediction [9][10], as a tool for dealing with vagueness and uncertainty of facts, and in classification.…”

Section: Basic Concepts Of Rough Set Theorymentioning

confidence: 99%

A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

Wang

Zhang

et al. 2007

Computational Science – ICCS 2007

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Abstract. In this paper, a new hybrid approach is presented to analyze factors affecting crude oil price using rough set and wavelet neural network. Related factors that affect crude oil price are found using text mining technique and Brent oil price is chosen as the decision price because it plays an important role in world crude oil markets. The relevant subsets of the factors are discovered by rough set module and the main factors are got, and then the important degrees of these are measured using wavelet neural network. Based on the novel hybrid approach, the predictability of crude oil price is discussed.

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Economic and financial prediction using rough sets model

Cited by 171 publications

References 21 publications

Review of Application Research of Support Vector Machines and Rough Sets in Credit Risk Evaluation

Review of Application Research of Support Vector Machines and Rough Sets in Credit Risk Evaluation

Evaluation of the Efficiency of Low Carbon Industrialization in Cultural and Natural Heritage: Taking Leshan as an Example

A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

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