Abstract
Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.
Highlights
Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, this theory is in a state of constant development
Thereafter, the importance degree can be normalized for easier comparison, showed in Figure 5, which can by the following equation: ( ) ∑ ( ) ∑ = Wij σ= cd Cij in=1 Cij, where Wij 1 (6)
We have identified a set of key internal and external uncertainties, which are eventually highlighted as “risk de terminants” based on their occurrence and consequential effects on the business performance
Summary
Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, this theory is in a state of constant development. (2016) Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach. M. Rahman tion and knowledge, and of is considered one of the first non-statistical approaches in data analysis (Pawlak, 1982) [1]. Rough set theory has become a valuable tool in the resolution of various problems, such as: representation of uncertain or imprecise knowledge; knowledge analysis; evaluation of quality and availability of information; identification and evaluation of data dependency; reasoning based an uncertain and reduct of information data. We describe the different risk factors of investment risk and find a big data approach to emphasize the significance risk factors to more smother way to invest. The key point of this paper is we can calculate the importance degree of different level risk factor from the inconsistent and incomplete data by rough set theory
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