The emerging financial pre-warning systems

[[abstract]]"The exact prediction of financial crises is an essential research task for decision makers. In recent years, data mining techniques have been used to tackle the related problems and perform a satisfactory job in various domains. However, in the information age, utilizing straightforward data mining techniques to predict financial crises has many shortcomings and limitations. Thus, this investigation utilized the random forest (RF) technique as a pre-processing procedure to determine the most representative features. Then, the selected features were fed into rough set theory to yield interpretable information for decision makers, who can use it to make suitable judgments in a turbulent economic climate. The proposed model is a promising alternative for predicting financial crisis, and it can assist in regard to both taxation and financial institutions.

Increment algorithm for attribute reduction based on improvement of discernibility matrix

研究目前粗糙集中求属性核和属性约简存在的效率低下问题,提出基于改进差别矩阵的核增量式更新算法,用于解决对象动态增加情况下核的更新问题.为降低现有增量式属性约简算法的时间和空间复杂度,提出一种不存储差别矩阵的高效属性约简算法,用于处理对象动态增加情况下属性约简的更新问题.理论及实验结果表明,该算法可明显降低时间和空间的复杂度.An incremental updating algorithm for computing core based on an improved discernibility matrix definition is proposed to improve the efficiency of computing attribute core and attribute reduction in rough sets.This new algorithm is mainly used to solve core updating when objects are dynamically increased.The purpose of this said algorithm is to decrease the complexity of time and space on the existing incremental attribute reduction algorithm.The discernibility matrix is not necessarry to be stored and therefore the attribute reduction is updated when objects are dynamically increased.Theoretical analysis and experimental results have shown that this new algorithm is feasible and effective.国家自然科学基金资助项目(50604012)---

A Novel Approach of Rough Conditional Entropy-Based Attribute Selection for Incomplete Decision System

Pawlak's classical rough set theory has been applied in analyzing ordinary information systems and decision systems. However, few studies have been carried out on the attribute selection problem in incomplete decision systems because of its complexity. It is therefore necessary to investigate effective algorithms to deal with this issue. In this paper, a new rough conditional entropy-based uncertainty measure is introduced to evaluate the significance of subsets of attributes in incomplete decision systems. Furthermore, some important properties of rough conditional entropy are derived and three attribute selection approaches are constructed, including an exhaustive search strategy approach, a heuristic search strategy approach, and a probabilistic search strategy approach for incomplete decision systems. Moreover, several experiments on real-life incomplete data sets are conducted to assess the efficiency of the proposed approaches. The final experimental results indicate that two of these approaches can give satisfying performances in the process of attribute selection in incomplete decision systems

Rough Set Approach to Incomplete Multiscale Information System

Multiscale information system is a new knowledge representation system for expressing the knowledge with different levels of granulations. In this paper, by considering the unknown values, which can be seen everywhere in real world applications, the incomplete multiscale information system is firstly investigated. The descriptor technique is employed to construct rough sets at different scales for analyzing the hierarchically structured data. The problem of unravelling decision rules at different scales is also addressed. Finally, the reduct descriptors are formulated to simplify decision rules, which can be derived from different scales. Some numerical examples are employed to substantiate the conceptual arguments