基于粗糙集理论的决策树方法在贷款客户信用评估中的应用

决策树是数据挖掘中常用的分类技术，其生成的规则便于决策者理解和应用。然而面对较多的属性且含有冗余和噪声属性的记录集生成的决策树时，无法删除冗余属性，造成运算过程复杂。本文旨在通过应用粗糙集理论，将其与决策树方法进行结合，对属性进行约简，降低运算复杂度，并生成相对简化的规则形式，并将其应用到银行个人贷款客户信用评估之中。     

Estimation of the mean in ranked set sampling with non responses

The estimation of the population mean when ranked set sampling [rss] is used for selecting the sample and non responses [nr] are present, is studied. The nr stratum is sub sampled using simple random sampling with replacement. Two strategies are analyzed. One of them is based on the selection of a sub sample from the nr in each cycle. The other uses sub samples selected among the nr in each rank.  The accuracy of the proposed estimators is characterized by the corresponding expected variances. Simulations and real life data are used for analyzing the behavior of them. Acknowledgements: This paper was developed partially during the visit of the author to Université des Antilles et Gouyane. The author gratefully acknowledges the helpful suggestions of the referees and thanks the support of DAAD for visiting Humboldt University where a version of the paper version was made.     

Stop and Go Agricultural Policies with a Land Market

This article studies the design of farm policy in the presence of asymmetric information about farmers' productivity, a government objective to insure farmers a minimum "parity" income, an endogenous land rent, and diminishing returns on alternative (nonprogram crop) land uses. In this setting, acreage set asides are never part of an optimal farm policy, although compensated acreage limits are. When there are new farmer entrants who cannot be excluded from farm programs, optimal policy takes the form of a pure voluntary acreage limitation—or "buyout"—program in which high-cost producers participate and low-cost producers do not.     

基于集对分析法的地区大中型工业企业自主创新能力评价

为了对地区大中型工业企业自主创新能力进行全面、科学的评价,基于集对分析法,提出了地区大中型工业企业自主创新能力评价指标体系及综合评价模型。通过对我国30个省市区大中型工业企业的实证分析,表明该方法科学简便,具有很好的实用价值。     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

基于模糊概念的可疑金融交易量化关联规则研究

从模糊集的基本理论入手,通过定义模糊概念软化属性域的划分边界,提出了一种新的基于模糊概念的量化关联规则方法.本方法克服了因划分区间而造成数据缺失的不足.最后通过将某市2004年的实际数据运用到建立的算法中,验证算法的有效性,为有效开展可疑金融交易识别提供了有益的参考.     

On the non-emptiness of the Mas-Colell bargaining set

We introduce an extension of the Mas-Colell bargaining set and construct, by an elaboration on a voting paradox, a superadditive four-person non-transferable utility game whose extended bargaining set is empty. It is shown that this extension constitutes an upper hemicontinuous correspondence. We conclude that the Mas-Colell bargaining set of a non-levelled superadditive NTU game may be empty.     

一种基于佳点集遗传算法的物流配送网络优化

首先根据实际问题分析了物流配送网络优化模型的各个关键组成部分,包括优化目标、决策变量和约束条件,并针对目前物流配送网络优化算法中存在的一些问题提出了一种新的算法,其核心是佳点集遗传算法。该算法编码采用prufer num ber结构,变异和交叉概率自适应选择。     

品牌进入消费者考虑集的决策规则——基于牙膏产品品牌的实证研究

消费者考虑集是消费者决策过程中的重要阶段，品牌只有进入考虑集才有可能被最终购买。本研究在上海以牙膏作为测试对象检验了牙膏品牌进入考虑集时消费者所依据的决策规则。实证结果显示，消费者主要使用排除式决策规则、非权重的补偿式决策规则和权重的补偿式决策规则。     

集值函数及向量值函数的两类线性算子逼近

本文得到了利用插值型线性算子列及积分型算子列逼近连续集值函数及连续向量值函数的收敛逼近阶。