A Fuzzy Approach to Classification of Text Documents

This paper discusses the classification problems of text documents. Based on the concept of the proximity degree, the set of words is partitioned into some equivalence classes.Particularly, the concepts of the semantic field and association degree are given in this paper.Based on the above concepts, this paper presents a fuzzy classification approach for document categorization. Furthermore, applying the concept of the entropy of information, the approaches to select key words from the set of words covering the classification of documents and to construct the hierarchical structure of key words are obtained.     

Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning

Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users.The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem.We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.     

Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system

ObjectiveTo develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next 10 years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system.MethodsLinguistic fuzzy rule-based classification systems are used, since they provide a good classification rate and a highly interpretable model. More specifically, a new methodology to combine fuzzy rule-based classification systems with interval-valued fuzzy sets is proposed, which is composed of three steps: (1) the modelling of the linguistic labels of the classifier using interval-valued fuzzy sets; (2) the use of the K_α operator in the inference process and (3) the application of a genetic tuning to find the best ignorance degree that each interval-valued fuzzy set represents as well as the best value for the parameter α of the K_α operator in each rule.ResultsThe suitability of the new proposal to deal with this medical diagnosis classification problem is shown by comparing its performance with respect to the one provided by two classical fuzzy classifiers and a previous interval-valued fuzzy rule-based classification system. The performance of the new method is statistically better than the ones obtained with the methods considered in the comparison. The new proposal enhances both the total number of correctly diagnosed patients, around 3% with respect the classical fuzzy classifiers and around 1% vs. the previous interval-valued fuzzy classifier, and the classifier ability to correctly differentiate patients of the different risk categories.ConclusionThe proposed methodology is a suitable tool to face the medical diagnosis of cardiovascular diseases, since it obtains a good classification rate and it also provides an interpretable model that can be easily understood by the doctors.     

基于模糊分类的快速分形图像编码方法

针对分形图像编码时间长的问题,提出选取边缘提取图像的6个特征参数作为图像块特征．采用模糊模式识别技术对图像块进行分类,后采用基于局部灰度均值聚类技术进一步减小最佳匹配搜索范围的两步分类法．实验表明,该方法可较大提高编码速度,解码质量无明显下降．     

A formal approach for graphically building fuzzy XML model

eXtensible Markup Language (XML) has been the de facto standard of data representation and exchange over the Web. In addition, imprecise and uncertain data are inherent in the real world. Although fuzzy data have been extensively investigated in the context of the relational model, the classical relational database model and its fuzzy extension to date do not satisfy the need of modeling complex objects with imprecision and uncertainty on the Web. On the basis of possibility theory, this paper concentrates on fuzzy information modeling in the fuzzy XML model and the fuzzy IFO model. In particular, the formal approach to mapping a fuzzy IFO model to a fuzzy document-type definition model is developed.     

Product classification in the Internet business—a fuzzy approach

In this paper, a methodology has been introduced as a decision support tool to the consumers in the Internet business. This decision support tool takes into account the multiple attributes of the product, analyses them with respect to the consumer's desire, and finally classifies these products into different hierarchical levels as per the consumer's level of preference. The product attributes, which are in general conflicting, imprecise, and non-commensurable in nature, are well handled here by using the concepts of fuzzy logic. Concepts of linguistic quantifier are used to quantify the qualitatively defined items and also to classify the products into different preference levels as required by the customer. Classification of the products into preference levels in any business, particularly, in the business through the Internet, gives a boost to the customer and helps him in a final product choice. The procedure described here can be used by virtual buying agents for generating a hierarchical classification based on buyer's preference. At the end, a numerical example is illustrated to highlight the procedure.     

Generating fuzzy rules from training instances for fuzzy classification systems

In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris data classification problem based on the attribute threshold value α, the classification threshold value β and the level threshold value γ, where α  [0, 1], β  [0, 1] and γ  [0, 1]. The proposed method gets a higher average classification accuracy rate than the existing methods.     

A CBR-based fuzzy decision tree approach for database classification

Database classification suffers from two well-known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a fuzzy decision tree (FDT), and genetic algorithms (GAs) to construct a decision-making system for data classification in various database applications. The model is major based on the idea that the historic database can be transformed into a smaller case base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller case-based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.     

基于模糊集的风险聚类预测方法

复杂社会技术系统存在许多不确定性的因素,这些因素给社会决策、项目过程管理带来了巨大的障碍和风险,因此有效的风险预测方法变得十分重要.根据风险项目的风险因素向量,利用模糊等价类的方法,对风险项目的历史数据进行模糊聚类,进而通过对新的风险项目和历史数据的模糊匹配实现了项目的风险聚类预测方法.分析和实践表明,该模型有效地解决了风险项目中诸多不确定性因素分类问题.该方法适合于政府决策、电子商务、软件项目管理等方面的风险管理应用.     

Categorical approach to the construction of fuzzy graph grammars

The categorical approach is proposed to the formalization of fuzzy graph grammars obtained as a result of generalization of sequential graph grammars. This approach takes into consideration the basic types of fuzziness that arise in constructing categories of fuzzy objects and describing transformations of fuzzy graphs generated by fuzzy sets. All the problems of undecidability that are well known for Chomsky grammars are proved to hold true for fuzzy graph grammars. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 130–144, July–August 2006.     

Finding the maximal membership in a fuzzy set of an element from another fuzzy set

The problem of finding the maximal membership grade in a fuzzy set of an element from another fuzzy set is an important class of optimisation problems manifested in the real world by situations in which we try to find what is the optimal financial satisfaction we can get from a socially responsible investment. Here, we provide a solution to this problem. We then look at the proposed solution for fuzzy sets with various types of membership grades, ordinal, interval value and intuitionistic.     

Intelligent reasoning using general knowledge to update specific information: A database approach

This paper describes a database framework which is similar to a relational database in style but uses alternative knowledge structures to represent uncertain data. Two knowledge structures are used, the mass assignment to represent probabilistic information and fuzzy sets to hold subjective information. We describe how the query is modified such that the selection criteria is held in the form of specific knowledge which can be updated with the more general knowledge held in the database. The updating procedure has the effect of filling in uncertain or missing information such that a final solution can be found. The operations required to perform a query are generated automatically, optimisation is performed as the operations are determined. The output from the database is in the form of a distribution over a projection of the database domain space. An example is given where a database of sea vessels can be given uncertain or noisy evidence about the characteristics of a vessel and a distribution of the likelihood of each of the vessels can be determined from the evidence.     

A coloring fuzzy graph approach for image classification

One of the main problems in practice is the difficulty in dealing with membership functions. Many decision makers ask for a graphical representation to help them to visualize results. In this paper, we point out that some useful tools for fuzzy classification can be derived from fuzzy coloring procedures. In particular, we bring here a crisp grey coloring algorithm based upon a sequential application of a basic black and white binary coloring procedure, already introduced in a previous paper [D. Gómez, J. Montero, J. Yáñez, C. Poidomani, A graph coloring algorithm approach for image segmentation, Omega, in press]. In this article, the image is conceived as a fuzzy graph defined on the set of pixels where fuzzy edges represent the distance between pixels. In this way, we can obtain a more flexible hierarchical structure of colors, which in turn should give useful hints about those classes with unclear boundaries.     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.     

A fuzzy approach to analogical reasoning

 We address the problem of the representation of resemblances involved in analogical reasoning. We use fuzzy relations to compare situations. We provide constructive methods to adapt the solution of an already solved situation to a similar new situation according to the degree of resemblance between these two situations. We give a general definition of analogical scheme which can be considered from a more or less constrained point of view.     

二型模糊集的模糊熵研究

研究基于质心的二型模糊集的模糊熵和加权模糊熵,构造了两个二型模糊集的模糊熵度量.针对二型模糊集的特殊情形,提出一种新的区间值模糊集的模糊熵度量,既弥补了现有区间值模糊集退化为普通模糊集时熵为零的不足,又克服了两个明显不同的区间值模糊集熵相等的缺点.数值实例和仿真实验表明了所提出模糊熵的合理性和实用性.