基于双论域模糊粗糙集的应急决策模型与方法

以突发危机事件应急决策为应用背景,讨论了双论域上模糊粗糙集的基本理论,建立了基于模糊相容关系的双论域模糊粗糙集模型. 在此基础上,把突发危机事件应急决策转化为一个具有模糊决策对象的双论域决策近似空间上的粗糙近似问题,构建了基于双论域模糊粗糙集的应急决策模型.首先在双论域近似空间中计算模糊决策对象的上(下)近似,进而结合经典非确定型决策的思想给出了突发危机事件应急决策的规则.同时,给出了模型的算法.该模型给出了一种在不完全信息环境下应急决策的方法,给出了在充分考虑决策者个人偏好信息基础上的决策置信度以及最优决策规则.该方法能够比较充分地符合应急决策信息不充分、资源有限以及时间紧迫的基本特征, 进而对突发危机事件应急决策提供科学的理论基础和现实的决策方法.最后,通过应用算例说明了模型的应用过程,结果验证了本文给出模型的有效性。     

Supporting triage of children with abdominal pain in the emergency room

The paper describes a methodology used for selecting the most relevant clinical features and for generating decision rules based on selected attributes from a medical data set with missing values. These rules will help emergency room (ER) medical personnel in triage (initial assessment) of children with abdominal pain. Presented approach is based on rough set theory extended with the ability of handling missing values and with the fuzzy measures allowing estimation of a value of information brought by particular attributes. The proposed methodology was applied for analyzing the data set containing records of patients with abdominal pain, collected in the emergency room of the cooperating hospital. Generated rules will be embedded into a computer decision support system that will be used in the emergency room. The system based on results of presented approach should allow improving of triage accuracy by the emergency room staff, and reducing management costs.     

Rough sets theory for multicriteria decision analysis

The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identified with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.     

Rough sets for pattern classification using pairwise-comparison-based tables

Rough set theory is a useful mathematical tool to deal with vagueness and uncertainty in available information. The results of a rough set approach are usually presented in the form of a set of decision rules derived from a decision table. Because using the original decision table is not the only way to implement a rough set approach, it could be interesting to investigate possible improvement in classification performance by replacing the original table with an alternative table obtained by pairwise comparisons among patterns. In this paper, a decision table based on pairwise comparisons is generated using the preference relation as in the Preference Ranking Organization Methods for Enrichment Evaluations (PROMETHEE) methods, to gauges the intensity of preference for one pattern over another pattern on each criterion before classification. The rough-set-based rule classifier (RSRC) provided by the well-known library for the Rough Set Exploration System (RSES) running under Windows as been successfully used to generate decision rules by using the pairwise-comparisons-based tables. Specifically, parameters related to the preference function on each criterion have been determined using a genetic-algorithm-based approach. Computer simulations involving several real-world data sets have revealed that of the proposed classification method performs well compared to other well-known classification methods and to RSRC using the original tables.     

基于粗糙集的经济预警规则发现

应用粗糙集的理论和方法对经济预警有关数据进行分析,挖掘其中隐含的有用信息,提取规则并对规则进行约简,从而求取表达经济预警信息的最小决策规则,为经济预警有用信息的获取提供一种有效的方法.     

Fuzzy rough sets and multiple-premise gradual decision rules

We propose a new fuzzy rough set approach which, differently from most known fuzzy set extensions of rough set theory, does not use any fuzzy logical connectives (t-norm, t-conorm, fuzzy implication). As there is no rationale for a particular choice of these connectives, avoiding this choice permits to reduce the part of arbitrary in the fuzzy rough approximation. Another advantage of the new approach is that it is based on the ordinal properties of fuzzy membership degrees only. The concepts of fuzzy lower and upper approximations are thus proposed, creating a base for induction of fuzzy decision rules having syntax and semantics of gradual rules. The proposed approach to rule induction is also interesting from the viewpoint of philosophy supporting data mining and knowledge discovery, because it is concordant with the method of concomitant variations by John Stuart Mill. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility degrees of multiple premises, on one hand, and conclusion, on the other hand.     

Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes

Rough set theory is a new data mining approach to manage vagueness. It is capable to discover important facts hidden in the data. Literature indicate the current rough set based approaches can’t guarantee that classification of a decision table is credible and it is not able to generate robust decision rules when new attributes are incrementally added in. In this study, an incremental attribute oriented rule-extraction algorithm is proposed to solve this deficiency commonly observed in the literature related to decision rule induction. The proposed approach considers incremental attributes based on the alternative rule extraction algorithm (AREA), which was presented for discovering preference-based rules according to the reducts with the maximum of strength index (SI), specifically the case that the desired reducts are not necessarily unique since several reducts could include the same value of SI. Using the AREA, an alternative rule can be defined as the rule which holds identical preference to the original decision rule and may be more attractive to a decision-maker than the original one. Through implementing the proposed approach, it can be effectively operating with new attributes to be added in the database/information systems. It is not required to re-compute the updated data set similar to the first step at the initial stage. The proposed algorithm also excludes these repetitive rules during the solution search stage since most of the rule induction approaches generate the repetitive rules. The proposed approach is capable to efficiently and effectively generate the complete, robust and non-repetitive decision rules. The rules derived from the data set provide an indication of how to effectively study this problem in further investigations.     

考虑供应链影响时基于粗糙集方法的企业信贷风险评估

对于考虑供应链时的企业信贷风险评估问题,提出基于粗糙集的解决办法.首先,根据样本数据建立决策信息表;然后采用等间距法对决策信息表的连续属性值进行离散化,并且应用辨识矩阵求出最小约简;最后,应用启发式值约简算法求出决策规则.试验计算结果表明,所提出的方法对企业的信贷等级能够进行有效的预测.     

Technical diagnostic of a fleet of vehicles using rough set theory

The paper presents a process of technical diagnostic applied to a fleet of vehicles utilized in the delivery system of express mail. It is focused on evaluation of diagnostic capacity of particular characteristics, reduction of a set of initially selected characteristics to a minimal and satisfactory subset, recognition of a technical condition of vehicles resulting in their condition-based classification. In addition, the decision rules facilitating technical diagnostic and management of a fleet of vehicles are generated and utilized. N-fold cross validation is applied to estimate the efficiency of the decision rules. The rough set theory is applied to support the diagnostic process of vehicles. Classical rough set (CRS) theory is compared with the dominance-based rough set (DRS) approach. The results of computational experiments for both approaches are compared.