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Granular computing is gradually changing from a label to a new field of study. The driving forces, the major schools of thought, and the future research directions on granular computing are examined. A triarchic theory of granular computing is outlined. Granular computing is viewed as an interdisciplinary study of human-inspired computing, characterized by structured thinking, structured problem solving,...
Data mining plays a central role in knowledge discovery. It involves applying specific algorithms to extract patterns or rules from data sets in a particular representation. Many researchers in database and machine-learning fields are interested in this new research topic since it offers opportunities to discover useful information and important relevant patterns in large databases, thus helping decision-makers...
In real-world applications, assigning labels to examples usually requires human effort and therefore, labeled training examples are expensive; unlabeled training examples, however, are cheap and abundant. As a consequence, semi-supervised learning which attempts to exploit unlabeled data to help improve learning performance has become a very hot topic in machine learning and data mining. In this talk,...
In this paper, modular neural network structure with fast training/recognition algorithm for pattern recognition task decomposition is presented. After the modular neural network is described, a new training algorithm, named non-gradient (NG) training algorithm, is proposed to train the sub-modules. The inputs error of the output layer is taken into account. Four classes of solution equations for...
Many feature selection methods have been proposed and most of them are in the supervised learning paradigm. Recently unsupervised feature selection has attracted a lot of attention especially in bioinformatics and text mining. So far, supervised feature selection and unsupervised feature selection method are studied and developed separately. A subset selected by a supervised feature selection method...
Granular computing is an emerging computing paradigm of information processing. It concerns the processing of complex information entities, called ldquoinformation granulesrdquo, which appear in the process of data abstraction and derivation of knowledge from information. The granular computing paradigm has been applied to many applications and we will address the application of granular computing...
Aiming at diversity being a necessary condition of the ensemble learning, we study method for improving diversity of the neural networks ensemble based on K-means clustering technique. In this paper, we propose a selecting approach that is first to train many classifiers through training set with neural network algorithm, and to classify data on validation set using classifiers. And then we use the...
Locally linear embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and growing neural gas (GNG), named growing locally linear embedding (GLLE). The proposed algorithm is able to preserve the global topological structures...
Nowadays most search engine like Google, Baidu, demonstrate their query results by the value of item, listing them in several pages. As we are now in an age of information explosion, the number of pages will be huge and users have to glance over several before they get what they want. If we cluster the results, this problem will be solved. There are several clustering methods, but not quite accurate...
This paper presents an approach to deal with multi sensor data fusion problem in incomplete circumstance using combination of granule idea, rough approximation and evidence theory. It deletes redundant sensors through rough set theory in selecting and reducing features, and forming dominant characters to form various granules. It applies these granules to establish belief functions to get different...
For the development of distance education, the update of interactive means is one of the important symbols. This paper mainly probes into the technology of Web page annotation for e-learning, studies on the key technologies of resource annotation and how to implement the interaction between learners and learning resources. In this paper, the model of resource interaction is put forward and the main...
Service composition provides a flexible way to quickly enable new application functionalities in next generation networks. Composite services are turned to be more and more complex, and the performance analysis of composite service is an important issue for service providers who want to get better QoS. In this paper, we proposed a stochastic Petri net based approach for modeling Web service composition...
Highly unbalanced data sets occur frequently in many practical applications and quite often the class of interest in such data sets is just a minority class. Like most standard machine learning methods, traditional rough sets based rule learning algorithms do not usually work well on highly unbalanced data sets. In this paper, we present a minority class rule learning algorithm for a highly unbalanced...
Based on a given probability space equipped with an increasing filtration, we estimate the mean square errors of numerical solutions of BSDE and reflected BSDE with the general Lipschitz condition, and proof the stability of them.
The following topics are dealt with: data mining; granular computing; information retrieval; fuzzy set; rough set; feature selection; ontology; pattern clustering and Web page.
Summary form only given. When employing the views of granular computing to the studies of artificial intelligence, it can be seen that there have been various levels of granule existed, system level, element level, and sub-element level, etc., each of which has its own interest of research. As for the system level of granule is concerned, there have also been various views of angle among which structural...
In this paper, we investigate the issues of positional definability of a concept in the framework of first-order logic. The positional definability of a concept means that the concept can be defined using social positions. While social positions can be induced from social relations via social network analysis, our results show that the definability of a concept using the underlying social relations,...
This paper is devoted to the discussion of necessary and sufficient conditions for isomorphism(the same structure) of fuzzy sets. epsiv - similarity of fuzzy sets is proposed. A new method to prove epsiv - similarity of fuzzy equivalence relation is given, which is more generalized than those in the references. Some conclusions further about fuzzy quotient space for arbitrary threshold are extended.
We study the problem of detecting and profiling terrorists using a combination of ordinary flat classifiers and relational information. Our starting point is a database for a set of individuals characterized by both ldquolocalrdquo attributes such as age and criminal background, and ldquorelationalrdquo information such as communications among a subset of the individuals. A subset of the individuals...
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