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A classifier model for satellite image data by using Partitioned-Feature based Classifier (PFC)is proposed in this paper. The PFC does not use concatenated feature vectors extracted from the original data at once to classify each datum, but uses extracted feature vectors to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must...
The structure in the multiple tables is so complex that we should not only improve the efficiency, but also insure the accuracy of classification when we classify the data. Some existing classification algorithms have good results in terms of the efficiency and the accuracy, for example: an efficient multi-relational Bayesian classifier based on the semantic relationship graph. But how to get the...
Back propagation neural network, as a method of data fusion technology, has been used in many common fields widely. In this paper, back propagation neural network based on binary full codes has been presented. Traditional back propagation method may classify the data as a good result, but it does not recognize the untrained data exactly. Back propagation neural network based on binary full codes method...
A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g. gathered from a thorough log file analysis, can be used to train classifiers which aim to inference relevance of retrieved documents based on user interactions. A problem in this approach is, however, the large imbalance...
In this paper, we present an approach for detection of spam calls over IP telephony called SPIT in VoIP systems. SPIT detection is different from spam detection in email in that the process has to be soft real-time, fewer features are available for examination due to the difficulty of mining voice traffic at runtime, and similarity in signaling traffic between legitimate and malicious callers. Our...
Recently, the discovery of deep Web data source and domain-relevant issue attract more and more attentions. This paper proposed a method using multi-classifier to discover and classify the data source of deep Web. Firstly, It used naive Bayes classifier to class the page into domain relevance or not. Secondly, improved C4.5 decision tree algorithm was used to identify the query interface. The result...
Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses...
Intertransaction class association rule mining (CARM) is an efficient method to predict the stock movement using the data of many stocks within a few days. And a crisp intertransaction CARM method based on genetic network programming (GNP) has been studied in our previous study. In this paper, a fuzzy intertransacion CARM method is presented to reduce the loss of information in discretization and...
The method of cluster analysis is usually adopted in spatial data mining research, and this paper studies the theory of the two popular methods of cluster analysis, investigates the interesting ratio of 108 features and attributes in spatial database, uses these two methods respectively to analyze the statistics data and compare the two analysis to get the close results, at last, classifies the current...
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,...
Inverse Synthetic Aperture Radar (ISAR) images are often used for classifying and recognising targets. Moreover the use of a fully polarimentric ISAR image enhances classiication capabilities. In this paper, the authors propose a novel ATR technique based on the use of fully polarimetric ISAR images and Neural Networks. In order to reduce the amount of data processed by the classifier, the brightest...
The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security.Based on the new proposed theoretical model of recognition space and further division method, this paper introduces a novel improvement of neural network classification: further division of recognition space(FDRS).Then studied the...
A significant roadblock to the use of genomic data for understanding gene networks in infectious pathogens is our inability to assign functionality to a large fraction of the genes. Nowhere is this more problematic than in the malaria parasite Plasmodium falciparum, in which 60% of the genes are annotated as "hypothetical". To circumvent this problem we proposed to employ wavelets, feature...
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