A data driven ensemble classifier for credit scoring analysis

This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied. This is essentially a classification task for credit scoring. Given its importance, many researchers have recently worked on an ensemble of classifiers. However, to the best of our knowledge, unrepresentative samples drastically reduce the accuracy of the deployment classifier. Few have attempted to preprocess the input samples into more homogeneous cluster groups and then fit the ensemble classifier accordingly. For this reason, we introduce the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier. This strategy would work better than a direct ensemble of classifiers without the preprocessing step. The proposed ensemble classifier is constructed by incorporating several data mining techniques, mainly involving optimal associate binning to discretize continuous values; neural network, support vector machine, and Bayesian network are used to augment the ensemble classifier. In particular, the Markov blanket concept of Bayesian network allows for a natural form of feature selection, which provides a basis for mining association rules. The learned knowledge is represented in multiple forms, including causal diagram and constrained association rules. The data driven nature of the proposed system distinguishes it from existing hybrid/ensemble credit scoring systems.     

Boosting an associative classifier

Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.     

Preprocessing expert system for mining association rules in telecommunication networks

Recently, the application of association rules mining becomes an important research area in alarm correlation analysis. However, the original alarms in the telecommunication networks cannot be used to mine association rules directly. This paper proposes a novel preprocessing expert system model to deal with the original alarms. This model uses two important techniques, of which the time window technique is used for converting original alarms into transactions, and the neural network technique can classify the alarms with different levels according to the characteristics of telecommunication networks in order to mine the weighted association rules. Simulation results and the real-world applications demonstrate the effectiveness and practicality of this preprocessing expert system.     

中药化学数据库关联规则的挖掘

数据挖掘是一种新兴的信息处理技术，本文将其中的关联规则运用到中药化学数据的处理，对其中的中医药效、植物科属、化学成分的活性、中药提取物现代药理等数据进行了维间关联规则的挖掘，找到了一系列的强规则，并对这些规则进行了分析，得到了其中有趣的关联规则，同时该关联规则的结果也说明了中药和西药在药效概念上的差异。该结果对于中药现代化，植物化学等相关的研究提供了一种新的思路。     

中医专家系统技术综述及新系统实现研究*

对中医专家系统二十多年来的发展进行了简单的概括,总结了专家系统应用在中医领域的技术特点。基于中医诊断专家系统的发展现状,分别从系统建模、知识获取和知识库构建等方面提出了新的思路和实现技术,例如认知模型的建立、领域本体的应用、自然语言理解、数据挖掘、知识网络、多智体agent技术等,并设计了一种新型中医专家系统,为中医专家系统的发展提供了有价值的参考方向。     

一种新的关联规则挖掘算法研究 *

:通过分析数据关联的特点和已有的关联规则挖掘算法 ,在定量描述的准确性和算法高效性方面作了进一步研究 ,提出了更准确的支持度和置信度定量描述方法和关联关系强弱的定量描述方法。同时 ,改进了 FP-growth挖掘算法 ,并应用于中医舌诊临床病例数据库挖掘实验中 ,可成功准确地提取中医舌诊诊断规则。测试结果表明该算法速度快、准确度高。     

分布式协同中医诊断系统的设计

利用“分布式协同专家系统开发工具ＢＩＴＡＩ－ＤＥＳＴ”,采用中医咳嗽诊断及胸痹诊断的专家知识,建立分布式协同的知识表示体系,分别构造各协同目标的规则及事实,生成一个具有实用意义的多智能体协同求解的中医诊断专家系统。     

利用关联规则建立专家系统的知识库

数据挖掘和专家系统同属人工智能领域。关联规则是数据挖掘的一种方法,它的最典型的应用是超市的购物篮分析。专家系统主要解决的是智能推理问题而关联规则侧重于各个数据项之间有价值的联系。通过对关联规则的Apriori算法及规则的产生方法进行改动,挖掘出可应用于专家系统的知识库中的决策规则,从而找出了利用关联规则挖掘出用于决策的规则的方法。     

恶意代码检测中的PrefixSpan*算法应用

结合数据挖掘和专家系统技术解决主机恶意代码检测问题,提出一个基于行为的恶意代码检测系统。数据挖掘算法采用改进的序列模式挖据算法——PrefixSpan*,该算法用简约投影数据库代替原PrefixSpan算法的投影数据库。PrefixSpan*从恶意代码行为序列库中挖掘关联规则,专家系统将获取的主机行为与规则匹配,从而达到检测恶意行为的目的。实验结果证明了该算法的正确性和有效性。     

基于聚类和模糊关联规则的中医药对量效分析*

以数据挖掘为技术手段,对方剂中药对的量效关联进行分析,主要工作包括：根据中药方剂中药物剂量分布的一般规律,用聚类方法自动划分药物剂量的模糊区间;基于模糊关联规则的概念,提出药对量效关联规则的挖掘算法;对所提出的算法进行了实现和验证。结果表明,基于聚类和模糊关联规则挖掘的中医药对量效关联分析符合中医药的基本特点,挖掘出的知识具有较高的正确率。