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In this paper we propose an image-based approach for in-vivo assessment of IVUS images. The method discriminates plaque components into four classes: calcium, necrotic core, fibrous and fibro-fatty. We employ the IVUS frames characterized by virtual histology (VH) for tissue labeling. As a result, we avoid the demerits of visual assessments of observers while at the same time the longitudinal resolution...
Blogosphere has become a hot research field in recent years. As the existing detection algorithm has problems of inefficient feature selection and weak correlation, we propose an algorithm of splog detection based on features relation tree. We could construct the tree according to the correlation of the features, reserving the strong relevance features and removing the weak ones, then prune the redundant...
Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological...
The high dimensionality of the text categorization raises big hurdles in applying many sophisticated learning algorithms to the text categorization. Feature selection, which reduces the number of features that represent documents, is an absolute requirement in text categorization. In this paper, we proposed a feature selection method, which improved the performance of the Ambiguity Measure feature...
This paper analyses the deficiency of SVM-RFE feature selection algorithm and puts forward a new feature selection method combined with SVM-RFE and PCA. Firstly, we get the optimal feature subset through the method of cross validation based on SVM-RFE. Then, we use the PCA method to analyse the main component about optimal feature subset and get a lower-dimension and independent data sets which are...
With the development of Web Service Technology, the quantity of the web services published on the Internet is increasing rapidly. Recognizing each web service intelligently becomes the key of efficiently using Internet. And the first step of recognization is to classify the web services accurately. To classify a huge amount of web services becomes a difficulty job. Therefore, in order to support applications...
Text categorization is the main issue which affects search results. Moreover, most approaches suffer from the high dimensionality of feature space. To overcome this problem, the use of feature selection techniques with statistical text categorization is investigated. The methods were evaluated based on Chi-Square, Information Gain and Gain Ratio. The data used to test the system consisted of 1,510...
Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel...
This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset...
A major difficulty of text categorization is extremely high dimensionality of text feature space. The use of feature selection techniques for large-scale text categorization task is desired for improving the accuracy and efficiency. χ2 statistic and simplified χ2 are two effective feature selection methods in text categorization. Using these two feature selection criteria, for a term, one needs to...
The dependency function proposed in the rough set model and fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that the relations between fuzzy memberships haven't used in the function. We introduce the concept of fuzzy similarity measure and propose a new model in evaluating feature dependency. The properties of the model were discussed. Experimental...
Feature selection is a very important step in text classification, which affects the accuracy and validity in text classification. Base on four classic feature selection algorithms of text mining, this paper has established a kind of integrated learning algorithm which applies with feature selection in text classification, in order to improve the accuracy of text classification. Test results show...
Recently Li et al. proposed a parameter selection method for Gaussian radial basis function (GRBF) in support vector machine (SVM). In his paper cosine similarity was calculated between two vectors based on the properties of GRBF kernel function. Li's method can determine an optimal sigma in SVM and thus efficiently improve its performance, yet it is limited by only focusing on a fixed original feature...
Text categorization is an important means to process automatically the information which increases exponentially. But due to the high dimensionality of the text corpus, many sophisticated classifiers can not be efficiently and effectively used in text categorization. So feature selection has become a research focus in text categorization. In this paper, we proposed a new feature selection, named SIE,...
A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable...
Methods currently used for micro-array data classification aim to select a minimum subset of features, namely a predictor, that is necessary to construct a classifier of best accuracy. Although effective, they lack in facing the primary goal of domain experts that are interested in detecting different groups of biologically relevant markers. In this paper, we present and test a framework which aims...
Feature selection (FS) is a classical combinatorial problem in pattern recognition and data mining. It finds major importance in classification and regression scenarios. In this paper, a hybrid approach that combines branch-and-bound (BB) search with Bhattacharya distance based feature selection is presented for classifying hyperspectral data using Support Vector Machine (SVM) classifiers. The performance...
In the recent years, Genome-Wide Association Study (GWAS) has been performed by many scientist around the world to find association between genetic profiles of different individuals with the risk of developing certain diseases. GWAS are performed using the Single Nucleotide Polymorphism (SNP) data which represents the genotypes of two different groups of individuals: the case group of individuals...
Ground cover classification using remotely sensed hyperspectral data is a challenging pattern recognition problem. The small (and expensive to collect) training sample sizes exacerbate the curse-of-dimensionality problem that already exists with such high dimensional feature spaces. However, Support Vector Machine (SVM) classifiers have been demonstrated to be better at handling such situations compared...
In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature...
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