A Generalized Rough Set Approach to Attribute Generalization in Data Mining

This paper presents a generalized method for updating approximations of a concept incrementally, which can be used as an effective tool to deal with dynamic attribute generalization. By combining this method and the LERS inductive learning algorithm, it also introduces a generalized quasi-incremental algorithm for learning classification rules from data bases.     

用于数据分析的变精度模糊粗糙模型

利用模糊集理论扩展Ziarko的变精度粗糙集模型,得到变精度模糊粗糙数据模型(VPFRDM).以属性重要性为启发式信息,提出了模糊信息系统中的属性约简方法.通过计算各模糊模式类相对于决策类的分类能力,生成相应的模糊规则.仿真实验表明,与Ziarko的变精度粗糙集方法相比,VPFRDM具有更好的数据概括能力.     

Kernel Generalized Noise Clustering Algorithm

IntroductionFuzzy clustering is one of the important methodsin pattern recognition. The most widely used fuzzyclustering is the fuzzy c-means (FCM) algorithm[1]which is conceived by Dunn[2]and generalized byBezdek[3]. Based on an objective function, the F…     

Less is More： Data Processing with SVM for Intrusion Detection

To improve the detection rate and lower down the false positive rate in intrusion detection system,dimensionality reduction is widely used in the intrusion detection system.For this purpose,a data processing (DP) with support vector machine (SVM) was built.Different from traditionally identifying the redundant data before purging the audit data by expert knowledge or utilizing different kinds of subsets of the available 41-connection attributes to build a classifier,the proposed strategy first removes the attributes whose correlation with another attribute exceeds a threshold,and then classifies two sequence samples as one class while removing either of the two samples whose similarity exceeds a threshold.The results of performance experiments showed that the strategy of DP and SVM is superior to the other existing data reduction strategies (e.g.,audit reduction,rule extraction,and feature selection),and that the detection model based on DP and SVM outperforms those based on data mining,soft computing,and hierarchical principal component analysis neural networks.     

一种改进的最小属性约简算法

最小属性约简是粗糙集理论中的一个重要内容,通过对属性约简算法的研究,指出基于U/{a}划分的最小属性约简算法中的不足,并提出了一种改进的最小属性约简算法.该算法以基于等价类的方法对论域进行压缩,有效地降低了算法的空间复杂度,用基于核的方法求解最小约简,从而保证可获得最小约简.实验结果表明,该算法具有复杂性低、存储空间小和效率高的特点,是一种有效的最小属性约简算法.     

Face recognition via adaptive image combination

Dimension reduction and manifold learning are the two most popular feature extraction methods. The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data, but no bases or features are more spatially localized than the original image pixels. So, adaptive image combination is presented to represent a class by a combined sample. The combined sample is a linear combination of original samples in the same class. Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance. Experimental results show that AIC is effective.     

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合理的票价分级策略往往能够使得公交乘客和经营者实现“双赢”的局面.为了制定合理的票价分级策略,本文首先通过SP调查采集乘客的选择意向信息,研究了影响乘客广义费用函数的因素变化规律,采用统计分析的方法建立了广义费用函数;其次,以乘客广义费用最小和公交经营者经营效益最大为目标,建立票价分级策略的双层优化模型;最后,基于遗传算法设计了该模型的求解方法,并以济南市2路公交做实例研究.结果表明,本文所研究的基于遗传算法的公交票价分级策略不仅能够使公交乘客的综合效用增加,而且能够使经营者的收益有所提高.     

改进可辨识矩阵的启发式知识约简及规则算法

首先提出了改进可辨识矩阵,然后对属性重要性进行了重新定义,并且将模糊聚类应用于属性重要性划分,从而在此基础上提出了一种启发式知识约简及决策规则提取算法.给出了新的不相容率的概念,用于描述知识数据库中数据不相容特征和修正提取的决策规则的决策错误率.经过实验,该算法具有优良的性能.     

一种基于知识的信息熵概念的粗糙集知识约简方法

文中提出了知识的信息熵的概念,通过知识的信息熵对属性的重要性进行了定义,提出了基于信息熵的租糙集知识约简方法,并通过实例进行了分析。分析结果表明文中提出的基于知识的信息熵概念的粗糙集知识约简方法是行之有效的。     

一种改进的有向无环图支持向量机分类算法

针对有向无环图支持向量机多类分类方法未采用有效的有向无环图生成算法,提出了一种改进的有向无环图生成算法。该方法采用了聚类分析中类距离的思想作为层次分类依据。实验结果表明,该方法与原方法相比具有较高的分类精度。