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Dempster-Shafer theory is a very powerful tool for data fusion, which provides a good estimation of imprecision, conflict from different sources and deal with any unions of hypotheses. In this paper, we propose to develop a high-performance hybrid Network Intrusion Detection System, based on belief functions. This system contains three levels, the first one includes two fast classifiers: Naïve Bayes...
This paper develops a novel learning-based method for detecting stereo saliency in stereopair images. The disparity maps computed from stereopair images provide an additional depth cue for stereo saliency detection. To the best of our knowledge, our approach is the first one to simultaneously detect the stereo saliency of both left and right images using support vector machine (SVM). In our work,...
In this paper a new islanding detection technique for grid-mode distributed-generation (DG) is proposed. Twenty one features are extracted from measurement of the voltage and frequency at the point of common coupling (PCC) in order to identify islanding occurrence with high accuracy. An IEEE 34-bus system was used in this paper to generate islanding and non-islanding training cases. Then a Support...
In several concept attainment systems, ranging from recommendation systems to information filtering, a sliding window of learning instances has been used in the learning process to allow the learner to follow concepts that change over time. However, no analytic study has been performed on the relation between the size of the sliding window and the performance of a learning system. In this work, we...
Electric load forecasting has received increasing attention over the years by academic and industrial researchers due to its major role for the effective and economic operation of power utilities. Least Support Vector Machine (LS SVM) is a new learning machine based on the statistical learning theory. A modelling approach based on least squares support vector machine (LS SVM) within the Bayesian evidence...
In this paper, the use of Independent Component Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the...
Proposed a method of fault diagnosis for cold storage system, the method based on probabilistic rough set and support vector machine(SVM). Simplify the uncertain information by using probabilistic rough set of Bayes decision making. Design a multi-level classifier of SVM to fault diagnosis. Research the typical fault set of cold storage system with the proposed method. The results show the accuracy...
This paper introduces a general Bayesian framework for obtaining sparse solutions to classify predicting, and the practical model 'relevance vector machine' (RVM) by Michael E. Tipping, which is applied in electric system transient stability assessment (TSA). As a bran-new thought of probabilistic learning model, it offers the superior level of generalization accuracy and a number of additional advantages...
This paper gives a deep investigation into AdaBoost algorithm, which is used to boost the performance of any given learning algorithm. Within AdaBoost, weak learners are crucial and primitive parts of the algorithm. Since weak learners are required to train with weights, two types of weak learners: artificial neural network weak learner and naive Bayes weak learner are designed. The results show AdaBoost...
In this paper, a new classification method based on relevance vector machine (RVM) is used in the MPSK signals classification. Compared with the support vector machine (SVM), RVM is sparse model in the Bayesian framework, not only the solution is highly sparse, but also it does not need to adjust model parameter and its kernel functions don't need to satisfy Mercer's condition. The fourth order cumulants...
This paper put forward a text categorization method based on Naive Bayes learning support vector machine. First adopt the text pre-processing. Then vector space model and linked list of technical are used to extract text features, reduce dimensions according to the characteristics of the text. Then after Naive Bayes algorithm been proposed to train the support vector machines, support vector machines...
The application of feature ranking to software engineering datasets is rare at best. In this study, we consider wrapper-based feature ranking where nine performance metrics aided by a particular learner are evaluated. We consider five learners and take two different approaches, each in conjunction with one of two different methodologies: 3-fold Cross-Validation (CV) and 3-fold Cross-Validation Risk...
Given the essential role of protein in life processes, computational assignment of protein functions has become one of the most important tasks in the area of bioinformatics. While Gene Ontology (GO) has been widely used in functional annotation, new approaches to address the problem of annotation incompleteness, which can leverage the support of the GO framework, are imminently required. In this...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
This paper presents the results of using statistical analysis and automatic text categorization to identify an author's age group based on the author's online chat posts. A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-score measurement of 0.996 on test data distinguishing teens from adults. We also introduce an alternative method...
Conventional support vector machine (SVM) utilizes the sign function to classify test data into different classes, which has demonstrated some limitations that hinder its performance. This paper explores the feasibility of using Bayesian statistics to support decision making in the SVM and demonstrated its application in Bioinformatics. The proposed methodology was tested on two real biological problems:...
A classification model is obtained after a classifier is trained on training data. Decision region is the region in which data are predicted the same class label by a classifier. Decision boundary is the boundary between regions of different classes. We view classification as dividing the data space into decision regions. The formal definitions of decision region and decision boundary are presented...
The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural...
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the email sender. A fast and accurate classifier is desirable in such an application. In this research, a highly scalable SVM modeling method, named Granular SVM with Random granulation (GSVM-RAND), is designed. GSVM-RAND applies...
Some techniques have been applied to improving software quality by classifying the software modules into fault-prone or non fault-prone categories. This can help developers focus on some high risk fault-prone modules. In this paper, a distribution-based Bayesian quadratic discriminant analysis (D-BQDA) technique is experimental investigated to identify software fault-prone modules. Experiments with...
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