A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. In this study, we provide a taxonomy and review of the fuzzy DEA methods. We present a classification scheme with four primary categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach and the possibility approach. We discuss each classification scheme and group the fuzzy DEA papers published in the literature over the past 20 years. To the best of our knowledge, this paper appears to be the only review and complete source of references on fuzzy DEA.     

A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application

Calibration refers to the adjustment of the posterior probabilities output by a classification algorithm towards the true prior probability distribution of the target classes. This adjustment is necessary to account for the difference in prior distributions between the training set and the test set. This article proposes a new calibration method, called the probability-mapping approach. Two types of mapping are proposed: linear and non-linear probability mapping. These new calibration techniques are applied to 9 real-life direct marketing datasets. The newly-proposed techniques are compared with the original, non-calibrated posterior probabilities and the adjusted posterior probabilities obtained using the rescaling algorithm of Saerens et al. (2002). The results recommend that marketing researchers must calibrate the posterior probabilities obtained from the classifier. Moreover, it is shown that using a ‘simple’ rescaling algorithm is not a first and workable solution, because the results suggest applying the newly-proposed non-linear probability-mapping approach for best calibration performance.     

Preference disaggregation and statistical learning for multicriteria decision support: A review

Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields.     

Applying integrated DEA/AHP to evaluate the economic performance of local governments in China

This paper aims at integrating data envelopment analysis (DEA) and analytic hierarchy process (AHP) to evaluate the economic development achieved by local governments in China. Since most similar evaluations are multi-objection problems, which both DEA and AHP are capable of solving, the integration of these two approaches is shown to be even more powerful. The proposed integrated DEA/AHP model can evaluate and rank different alternatives. In addition, a time-scale comparison of the economic performances of local governments in China was carried out using the malmquist productivity index (MPI), which indicated that there is a trend of economic growth. However, empirical results indicate that after discounting the advantages of location and political connections, the east district provinces of China do not have superior economic performance or a better MPI index, as compared with other districts. This result is contrary to our original hypothesis.     

多元地学信息挖掘中分层动量增项自适应BP算法应用研究

地球化学元素分布规律的研究是揭示元素矿化富集及空间变化规律的重要途径之一,采用人工神经网络方法对东天山地区地球化学信息进行了研究,为矿区靶区预测提供支持.对传统BP算法进行了改进,采用分层动量增项自适应BP算法设计,并完成了样本区数据的预测分析,效果较好.     

基于SFA与DEA模型的创新型城市科技创新效率实证研究

分别运用SFA模型和DEA模型对10个样本城市2001-2010年的科技创新效率进行定量测度,并对两种方法测度出的创新型城市科技创新效率值排序进行相关分析和一致性检验.结果表明两种方法测度出的创新型城市科技创新效率在数值上有显著差异,但在效率排序上具有很好的一致性.     

基于模糊属性刻画的T相似性关系的构造

模糊关系是模糊粗糙分析的基础。从属性数据生成模糊关系是模糊粗糙集实际应用中的重要问题。针对模糊属性刻画,给出了生成几种T相似性关系的方法。首先,对于每一个属性,分别生成一个T相似性关系。而后,通过聚合算子来合成这些T相似性关系,以得到一个综合的T相似性关系。     

基于特征选择的决策树方法在磷酸铝AlPO4-5定向合成中的应用

分子筛类开放骨架材料的合成与结构关系研究对实现这类材料的定向合成起着至关重要的作用. 本文在建立开放骨架磷酸铝合成反应数据库的基础上, 提出了利用基于特征选择的决策树(C5.0)方法, 考察了不同反应条件(即各反应特征参数)对磷酸铝分子筛AlPO4-5生成的影响. 基于决策树模型, 利用8个反应特征参数,可以有效预测磷酸铝分子筛AlPO4-5的生成, 准确率达到88.18%, 接收者操作特性(ROC)曲线下面积(AUC)达到90%. 研究结果表明, 在众多的反应特征参数中, 有机模板剂的几何尺寸参数, 特别是模板剂的次长距离, 是影响AlPO4-5分子筛合成的重要因素.     

基于LabVIEW的EAST纵场电源数据采集系统设计

基于LabVIEW软件和数据库设计了一套实时数据采集系统,实现了EAST纵场电源系统多信号的实时采集、数据处理、储存和历史查询。结果表明,设计的系统能够实现纵场电源系统状态的实时监控。