基于灰色关联聚类-粗糙集的民用建筑低碳环保数据指标体系构建

建立合理的低碳环保数据指标体系是评价和规范民用建筑节能环保的关键。在指标初选的基础上,运用灰色关联聚类-粗糙集进行约简分析,最终构建了包含建筑碳排放、绿化治理、大气环境、污水排放、固体废弃物、声环境6大方面的低碳环保数据指标体系。结果表明:运用灰色关联聚类-粗糙集在聚类分析和指标约简上的优势,可构建科学合理的低碳环保数据指标体系。结合后续的数据统计、计量分析,进一步指导民用建筑低碳环保政策措施的制定、节能环保效益的评价与管理等。

基于粗糙集的不完备信息系统的知识获取

根据不完备信息系统(IIS)的数据不完整或不完备的特性,从粗糙集(RS)等价类的概念出发,提出了基于粗糙集理论的不完整数据集知识获取方法,利用该算法不仅可以从不完整数据集中提取规则,并且能够解决在学习过程中对训练事例属性未知特征值的估计问题。最后,给出具体的算例利用所给的算法求得信息系统的知识获取,并对所得的结果进行比较,从而说明所给算法的有效性和实用性,也证实了该算法可以有效地应用于复杂工业过程的专家系统知识库的建立。

基于不可分辨关系的文本分裂层次聚类研究

为了对文本数据集实施高效聚类,笔者提出了一种基于不可分辨关系的分裂层次文本数据聚类算法。首先把文本集转化成布尔文本信息系统,然后在文本信息系统上定义不可分辨关系,最后根据文本对象间的最大不可分辨性对文本集进行分裂聚类,直到任意类中所有对象都不可分辨关系为止。实验结果表明,本算法高效、准确,具备可移植性。

Document classification approach by rough-set-based corner classification neural network

A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.

基于计算机视觉的图像分割新方法

针对杂波背景条件下图像分割精度较差的问题,提出了一种基于粗糙集模糊概率C均值聚类的Otsu目标图像分割方法.首先,在均值聚类的框架内分析了传统Otsu方法受样本方差影响较大的缺陷;然后,在模糊集理论的框架内引入粗糙集处理方法,通过对不精确数据的有效处理,克服背景方差效应,提升了图像分割的精度.实验结果表明,该方法较传统Otsu方法具有更高的分割精度和抗背景噪声干扰能力.

Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification

This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.

A minimal axiom group for rough set based on quasi-ordering

Rough set axiomatization is one aspect of rough set study to characterize rough set theory using dependable and minimal axiom groups. Thus, rough set theory can be studied by logic and axiom system methods. The classic rough set theory is based on equivalent relation, but rough set theory based on reflexive and transitive relation (called quasi-ordering) has wide applications in the real world. To characterize topological rough set theory, an axiom group named RT, consisting of 4 axioms, is proposed. It is proved that the axiom group reliability in characterizing rough set theory based on similar relation is reasonable. Simultaneously, the minimization of the axiom group, which requires that each axiom is an equation and each is independent, is proved. The axiom group is helpful for researching rough set theory by logic and axiom system methods.

Intrusion detection using rough set classification

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

Study based on ＂Situational Rationality＂ hypothesis for customer market classification model

The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hypothesis and then comes up with the market segmenting models and classification algorithm basing on this hypothesis. This algorithm combined the Rough Set theory and Neural Networks in application, which overcome the dilemma that caused complicated network structure and long training time by only using Neural Networks and influenced the classification precision caused by noise disturbance by only using Rough Set methods. Finally, the paper did a comparison experiment between the traditional method and the method we came up, the results shows that the model and algorithm has its advantage on every aspects.

THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams

The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.