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To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to...
The thesis proposes a hybrid intrusion detection model based on the parallel genetic algorithm and the rough set theory. Due to the difficult for the status of intrusion detection rules. This model, taking the advantage of rough set's streamline the edge to data and genetic algorithm's high parallelism, succeeds in introducing the genetic-rough set theory to the instrusion detection. The application...
Document feature extraction and classifier selection are two key problems for document classification approach. To effectively resolve the above two problems, a novel document classification algorithm is proposed by combining the merits of local fisher discriminant analysis and kernel logistic regression. Extensive experiments have been conducted, and the results demonstrate that the proposed algorithm...
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
Indirect immunofluorescence (IIF) with HEp-2 cells has been used to detect antinuclear auto-antibodies (ANA) for diagnosing systemic autoimmune diseases. The aim of this study is to develop an automatic scheme to identify the fluorescence pattern of HEp-2 cell in the IIF images. By using the previously proposed two-staged segmentation method, the similarity-based watershed algorithm with marker techniques...
Extracting classification rules from data is an important task of data mining and is gaining considerable attention in recent years. This paper comprises classification of different types of rule extraction algorithm and their comparative study by considering their advantages separately. These Ant Colony based algorithms called as Ant_Miner have been successfully implemented in various fields such...
In this paper, we propose a framework of general genre (e.g., action, comedy, drama, documentary, musical, etc...) movie video abstraction scheme for embedded devices based on pure audio. The proposed algorithm does chaptering of multi-genre movie videos by detecting silence, environmental noise, pure speech, music (pure instrumental music and music with vocals, i.e., songs), and speech with back...
Designing an OCR system for Indian languages in general is more complex than those of European languages due the linguistic complexity. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Telugu, a popular South Indian language. In this paper, we proposed a method for reliable extraction of text line, word and character from document images of Telugu...
The increasing complexity of modern distributed systems makes conventional fault tolerance and recovery prohibitively expensive. One of the promising approaches is online failure prediction. However, the process of feature extraction depends on the experienced administrators and their domain knowledge to filtering and compressing error events into a form that is easy for failure prediction. In this...
Recent developments in applied and heuristic optimization methods used for feature extraction from satellite images have been strongly influenced and inspired by natural and biological system. The findings of recent studies are showing strong evidence to the fact that some aspects of the biogeography can be applied to solve specific problems in science and engineering. The algorithm based on this...
Automatic identification of digital signal types is of interest for both civil and military applications. This paper presents an efficient signal type identifier that includes a variety of digital signals. In this method, a combination of spectral and statistical features are used as an input to the classifier. Also the features are weighted based on the degree of dispersion to increase the effect...
Recently, the ant colony optimization (ACO) meta-heuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the ??-component...
The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features...
This paper focuses on researches related to medical digital imaging of endoscopic gastritis. It provides suffice information on endoscopic procedure and types of gastritis. Besides that, it also briefly addressed feature extraction methods. Feature selection and Multiple Instance Learning (MIL) concept are also reviewed. As a conclusion, this paper become a basis to propose an improved artificial...
Techniques for information hiding and steganography are becoming increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages is also becoming considerably more difficult. In this paper, we describe a universal approach to steganalyse the least significant bit steganography method for detecting the presence of hidden messages embedded within...
Clustering is used to find out the objects that resemble each other and compose different groups, cluster analysis is an important job in data mining. This article brings the rough set into fuzzy cluster, by using the methods of attributes contracted in the rough set theory to improve the FCM algorithm; the improved algorithm had been proved a high precise ratio.
Under the framework of PU(Positive data and Unlabeled data), this paper originally proposes a three-setp algorithm. First, CoTraining is employed for filtering out the likely positive data from the unlabeled dataset U. Second, affinity propagation (AP) approach attempts to pick out the strong positive from likely positive set which is produced in first step. Those data picked out can be supplied to...
A detailed design and implementation of a Chinese Web-page classification system is described in this paper, and some methods on Chinese Web-page preprocessing and feature preparation are proposed. Experimental results on a Chinese Web-page dataset show that methods we designed can improve the performance from 75.82% to 81.88%.
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
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