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Data fusion is often used to eliminate data redundancy and prolong network lifetime in wireless sensor network (WSN), in order to solve the problems of the energy consumption and delay of WSN. This paper applies the mobile agent technology to data fusion. Firstly the model of WSN and structure of mobile agent were described, and then the data fusion routing algorithm is proposed, in which it uses...
Iris in human's eyes contains enrich texture information which is useful for identity authentication. A key and still open issue in iris recognition is how best representing such textural information by using a set of feature vectors. This paper proposes a new method for iris feature recognition by fusing 2D and ID features. This iris recognition system consisted of four major stages: Iris Preprocessing,...
Cluster ensemble information from local adaptive clustering (LAC) contains cluster membership of each instance and subspace of each cluster, which is more precise than clustering ensemble information from K-means clustering. However, clustering ensemble information is directly derived from LAC. This paper proposes an improved local adaptive clustering ensemble (LACE) based on link analysis. Link analysis...
Though there exist a lot of cluster ensemble approaches, few of them consider how to deal with noisy datasets. In this paper, we design a new noise immunization based cluster ensemble framework named as AP2CE to tackle the challenges raised by noisy datasets. AP2CE not only takes advantage of the affinity propagation algorithm (AP) and the normalized cut algorithm (Ncut), but also possesses the characteristics...
Based on the Bayesian learning principle (BayesMSDA), this paper presents a new multi-source domain adaptation framework, where one target domain and more than one source domains are used. In this framework, the label of a target data point is determined according to its posterior probability, which is calculated using the Bayesian formula. To fulfill this framework, a novel prior of the target domain...
A method of clustering ensemble is transforming the clustering ensemble problem into the clustering problem among objects in a nominal information table. The basic problem is to give a method which is used to calculate the distance between the nominal attribute value. In this paper, DILCA method is adopted to calculate the distance between the nominal attribute value. Using the correlation between...
In recent research, classification involving imbalanced datasets has received considerable attention. Most classification algorithms tend to predict that most of the incoming data belongs to the majority class, resulting in the poor classification performance in minority class instances, which are usually of much more interest. In this paper we propose a clustering-based subset ensemble learning method...
Multi-granulation rough sets are extended rough set models, which are based on a family of equivalence relations on universe of discourse. By applying join or intersection operations of binary relations, in this paper, two binary relations are defined in a multi-granulation approximation space, consequently two types of rough sets are introduced. Subsequently, in the views of approximation space fusion...
Named Entity Recognition (NER) is one of the most important problems in Natural Language Processing (NLP). NER also has a broad prospect for application and important research value. There are a lot of methods and technology to solve NER problem. In this paper, for a specific application background, a new multi-pattern fusion based semi-supervised NER method is proposed. We use soft-matching method...
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