Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach

Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Pardel, Przemyslaw Wiktor; Skowron, Andrzej; Sokołowska, Barbara

doi:10.1007/978-3-642-30341-8_7

Cited by 5 publications

(2 citation statements)

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“…Research by Pawlak [17] laid the foundation for using RST to handle uncertainty and imprecision in data. Subsequent studies, such as those by Yao and Herbert [18] and Bazan et al, [19], have demonstrated the efficacy of RST in financial decision-making, offering a structured approach for uncovering hidden patterns within complex datasets. Shen and Tzeng [20] have provided a series of financial applications using RST, integrated with other soft computing techniques, such as fuzzy inference or fuzzy integral [21].…”

Section: Rst Application In Financementioning

confidence: 99%

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

Shen

2024

Decis. Mak. Adv.

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Using a Rough-Set-based approach, this study investigates the connection between financial performance, Environmental, Social, and Governance (ESG) factors, and stock returns. It analyzes data from various companies and industries to examine how ESG metrics interact with financial indicators to influence stock prices. Those findings underscore the importance of considering financial and ESG factors when evaluating a company's performance and market valuation. The Rough-Set-based bipolar model comprises eight decision rules, and the DEMATEL (Decision-Making Trial and Evaluation Laboratory) analysis reveals the influence relationship among the core attributes. Furthermore, the ranking result, based on the bipolar model, is consistent with the market returns, which suggests the validity of this study.

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Section: Rst Application In Financementioning

confidence: 99%

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

Shen

2024

Decis. Mak. Adv.

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“…Command is in conformity with the protocol specification, but has violated the production logic process of industrial control system, the system is in a state of dangerous (such as the reaction kettle feeding valve and a discharge valve cannot open at the same time) [5][6].…”

Section: Process Attacksmentioning

confidence: 99%

An Industrial Control System Situation Awareness Method based on Weighting Algorithm

Shi¹,

Tu²,

Wang³

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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This article introduces a brand-new posture perception approach towards the industrial control system to improve the capability of its related network security monitoring. Took full account of the combination issue of multi-source heterogeneous information to accurately reflect the network security situation by extracting its characteristic information that aims to industrial control environment features. Leveraged factor weighting algorithm to extract the intrusion information flow and detect the correlation so that can achieve the prediction and perception of security situation. the final simulation result showed that the calculation approach has higher effective & real-time applied value with lower complexity.

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Erratum: CDM: A Prototype Implementation of the Data Mining JDM Standard

Lasek

2014

Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 30 – July 4, 2014,

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Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach

Cited by 5 publications

References 20 publications

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

The Exploring the Relationship between Financial Performance Indicators, ESG, and Stock Price Returns: A Rough Set-based Bipolar Approach

An Industrial Control System Situation Awareness Method based on Weighting Algorithm

Erratum: CDM: A Prototype Implementation of the Data Mining JDM Standard

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