Aggregating multiple classification results using Choquet integral for financial distress early warning

Financial distress prediction methods based on combination classifier become a rising trend in this field. This paper applies Choquet integral to ensemble single classifiers and proposes a Choquet integral-based combination classifier for financial distress early warning. Also, as the conditions between training and pattern recognition cannot be completely consistent, so this paper proposes an adaptive fuzzy measure by using the dynamic information in the single classifier pattern recognition results which is more reasonable than the static prior fuzzy density. Finally, a comparative analysis based on Chinese listed companies’ real data is conducted to verify prediction accuracy and stability of the combination classifier. The experiment results indicate that financial distress prediction using Choquet integral-based combination classifier has higher average accuracy and stability than single classifiers.     

An agent-based simulation model of enterprises financial distress for the enterprise of different life cycle stage

This paper proposes a new agent-based simulation model to simulate the causes and processes of enterprise financial distress. The general framework of the model including four agents, which are enterprise, product, bank and macro environment is described. By investigating the different causes of financial distress in enterprise’s different life cycle stages, we implement the simulation model to four specific cases which belong to start-up, growth, mature and decline life cycle stages respectively. Then a comparative analysis between our simulation results and real situation in four proposed specific cases is conducted, demonstrating that the proposed simulation model is a promising tool for comprehensively analyzing the causes and processes of financial distress.     

基于Choquet 积分的模糊数直觉模糊数多属性决策方法

根据模糊数直觉模糊数的运算法则,提出了基于模糊测度和Choquet积分的模糊数直觉模糊数的信息集成算子,并证明了该算子的相关性质.运用该算子研究了属性间具有关联性的、属性值为模糊数直觉模糊数的多属性决策方法,最后通过实例分析表明了所提出方法的有效性.     

基于广义Choquet积分的职位推荐算法

为解决传统职位推荐算法推荐结果范围小,无法对跨行就业率高的人群进行有效推荐的问题,提出基于人口统计学数据及广义Choquet积分的职位推荐算法。利用层次分析法构建用户信息层次结构模型,结合广义Shapley函数与离散Choquet积分,通过计算最优相似人群进行职位推荐。与传统模型实验对比,推荐模型在雷达招聘网站提供的数据集上准确率提高了8.35%,有效验证了其可行性和有效性。     

New aggregation operators based on the Choquet integral and 2-tuple linguistic information

Considered in this paper is the group decision making problem with inter-dependent or interactive attributes, where evaluation values of decision makers are in linguistic arguments. By using the Choquet integral, some new aggregation operators are introduced, including the 2-tuple correlated averaging operator, the 2-tuple correlated geometric operator and the generalized 2-tuple correlated averaging operator. The proposed operators can better reflect the correlations among the elements. After investigating properties of these operators, a new multiple attribute decision making method based on the new operators is proposed. Finally, a numerical example is provided to illustrate the feasibility and efficiency of the proposed method.     

A decision support system for fund raising management based on the Choquet integral methodology

The employment of a decision support system for optimizing fund raising strategies is crucial to the management of non‐profit organizations. Commonly considered methodologies utilize the organization's donor database in order to gather and analyse information. However, many organizations, especially small‐ and medium‐sized ones, do not own or efficiently manage a database, and consequently, the usual methods are inapplicable. In this paper, a decision support system is developed that is able to identify the most promising fund raising strategies on the basis of the organization's profile. The profile factors of a non‐profit organization are analysed and hierarchically organized in a decision tree in order to effectively employ the Choquet integral methodology, which is recommended in these kinds of multi‐criteria decision problems. The results obtained in the real operational context show the effectiveness of the proposed system.     

Financial distress prediction using the hybrid associative memory with translation

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.     

An interval-valued 2-tuple linguistic group decision-making model based on the Choquet integral operator

The Choquet integral (IL) operator is an effective approach for handling interdependence among decision attributes in complex decision-making problems. However, the fuzzy measures of attributes and attribute sets required by IL are difficult to achieve directly, which limits the application of IL. This paper proposes a new method for determining fuzzy measures of attributes by extending Marichal's concept of entropy for fuzzy measure. To well represent the assessment information, interval-valued 2-tuple linguistic context is utilised to represent information. Then, we propose a Choquet integral operator in an interval-valued 2-tuple linguistic environment, which can effectively handle the correlation between attributes. In addition, we apply these methods to solve multi-attribute group decision-making problems. The feasibility and validity of the proposed operator is demonstrated by comparisons with other models in illustrative example part.     

基于Choquet 积分的直觉不确定语言信息集结算子及其应用

基于直觉不确定语言信息,针对属性间不严格相互独立且具有较大关联度的群决策问题,提出了两种基于直觉不确定语言信息的Choquet积分算子.首先,分析了因属性关联使得以往直觉不确定语言信息集结算子失效的现象,对此引入模糊测度,提出了直觉不确定语言的Choquet加权算术平均算子(IULCWA)和直觉不确定语言的Choquet加权几何平均算子(IULCGM);然后,证明了算子的相关性质,研究了属性间相关的、属性值为直觉不确定语言数的多属性群决策方法;最后,通过实例分析说明了以往直觉不确定语言信息集结算子的局限性以及新算子的有效性.     

基于累积前景理论和Choquet积分的直觉梯形模糊多属性决策

针对属性值为直觉梯形模糊数且属性存在关联性的风险决策问题, 提出一种基于累积前景理论和Choquet积分的直觉梯形模糊多属性决策方法。根据直觉梯形模糊数距离公式定义了直觉梯形模糊信息的前景价值函数, 通过价值函数和决策权重函数计算方案单属性前景值, 并运用Choquet积分融合属性间存在关联性的前景价值信息获得方案综合前景值, 根据综合前景值的大小实现方案的排序和优选。风险投资实例分析说明了该方法的可行性。     

基于意见领袖的微博生命周期预测模型研究

为了有效研究社交网络中意见领袖在新浪微博传播所起的作用及微博的生命周期和传播模式,提出了一种OLB微博传播预测模型。首先通过爬取微博数据,进行数据分析;其次,拟合出与影响力有关的四个因素的数学表达式,并通过层次分析法给出权重计算方法;最后利用计算的影响力以及转发数与相关因素的关系构建出OLB模型,从而对意见领袖传播作用及微博生命周期进行实验预测分析。仿真结果表明,在微博信息传播中意见领袖影响力与其微博的传播作用成正比例关系,通过误差分析得到四组数据的平均误差值分别为1.0%、5.0%、2.4%及5.1%,提出的OLB模型对于预测微博传播模式合理、有效。     

Financial distress early warning based on group decision making

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts’ experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts’ knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.     

基于共识与Choquet积分的模糊Petri网可信度确定方法*

针对模糊Petri网可信度确定过程中,决策结果可接受程度较低和未考虑影响因子间关联的问题,提出基于群体共识测度与Choquet积分确定模糊Petri网可信度的方法。设计了基于决策者与群体评估信息偏差修正的自适应共识测度算法,获取可接受程度较高的群体评估信息。构建最大2-可加模糊测度Marichal熵模型求解影响因子间的交互作用系数,根据交互作用系数、默比乌斯变换和模糊测度之间的关系确定模糊测度,利用Choquet积分算子集结群体评估信息得到模糊Petri网可信度。将此方法用于确定燃气轮机故障诊断模糊Petri网输入库所可信度,并与未考虑共识测度和影响因子间关联的方法进行比较,计算结果验证了该方法的有效性和可行性。     

Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers

How to effectively predict financial distress is an important problem in corporate financial management. Though much attention has been paid to financial distress prediction methods based on single classifier, its limitation of uncertainty and benefit of multiple classifier combination for financial distress prediction has also been neglected. This paper puts forward a financial distress prediction method based on weighted majority voting combination of multiple classifiers. The framework of multiple classifier combination system, model of weighted majority voting combination, basic classifiers’ voting weight model and basic classifiers’ selection principles are discussed in detail. Empirical experiment with Chinese listed companies’ real world data indicates that this method can greatly improve the average prediction accuracy and stability, and it is more suitable for financial distress prediction than single classifiers.     

A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998–2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios—discriminant analysis and the prediction of corporate bankruptcy using capital market data, Journal of Finance 23 (4) (1968) 589–609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109–131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59–82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure.     

基于设备全生命周期成本的老旧变压器评估

本文以某运行近25年的老旧变电站500kV电力变压器为实例研究了变压器设备的全生命周期成本为实例,基于某该变电站运行多年以来的运行数据,对该站主变的材料、性能、运行工况进行分析,结合20年前生产制造的工艺、水平和技术能力,考虑包括初始投资成本、运行成本、检修维护成本、故障成本以及退役处置成本,对某变电站500kV主变压器的全寿命周期成本进行综合评估,并通过电力变压器对合资品牌以及国产优质品牌2个质量水平的产品进行分析比较,得出了设备购置成本对LCC的控制起到主导作用,在检修维护成本、故障成本以及退役处置成本占比不大的情况下,合资品牌产品较高的设备购置费用不利于成本的控制,国产优质品牌产品的全寿命周期成本相对更优的结论     

基于生命周期理论的软件项目投标风险评估

传统软件项目投标的风险评估往往局限在投标过程的某个阶段,且评估具有较强的主观性。针对此问题,本文基于项目生命周期理论,采用熵权系数法和AHP方法来确定各个风险因素和项目生命周期各阶段风险的相对权值,利用模糊综合评判法对软件项目投标的风险进行综合评估。实例分析表明：所建立的风险评估模型克服了主观判断的弊端,使投标者明确整个项目生命周期及生命周期各阶段的风险控制的重点领域,具有良好的适用性。     

CMM/CMMI与软件生命周期模型关系的研究

对CMM/CMMI和主流软件生命周期模型的关系进行了分析,认为:a)CMM/CMMI与软件生命周期模型都是软件活动过程化的产物.在软件过程化进程中,CMM/CMMI是软件过程化成熟期的成果;软件生命周期模型则是软件过程化前期的成果.b)由于发展背景不同,需要处理的问题各异,使得软件生命周期模型关注于工程活动;CMM/CMMI关注于软件开发活动的整体.c)尽管CMM/CMMI编写者的初衷是保持CMM/CMMI与特定软件生命周期模型的无关性,但是其产生的时代背景却使CMM/CMMI受到了软件生命周期模型的影响.     

基于多层面生命周期评价方法的产品结构优化

一个企业或园区通常拥有大量的产品可供投资,有多种产品生产链可供选择,构成一个包含多种产品和产品链的产品体系结构。应用上篇论文提出的多层面生命周期评价方法,并结合过程系统的．优化技术,可以对这样的复杂产品体系结构进行优化,最终得出满意的产品决策。本文以有机硅产品为例,介绍该方法在产品结构优化中的应用。