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Over the last few years, a large number of security patterns have been published. However, this large number of patterns led to a problem in selecting appropriate patterns for different security requirements. In this paper, we present an automatic selection approach for security patterns. We use text processing approach and learning techniques to select appropriate security patterns for given security...
Requirement tracing is an important activity for its helpfulness to effective system quality assurance, impact analyzing of changes and software maintenance. In this paper, we propose an automatic approach called LGRTL to recover traceability links between high-level requirements and low-level design elements. This approach treats the recovery process as Bayesian classification process. Meanwhile,...
A new term weighting approach is used to construct the simplest linear weighting classifier (SL). By probability standard deviation of terms as a base line weighting regulated with terms distributed parameters based on subjective logic reasoning, the weighting is computed. In the assessment process of terms distributed parameters, the model of the term reputation in documents categories based on Beta...
Concerning for air target attribute identification, an attribute identification method based on the combination of multi-class SVM and D-S evidence theory is proposed. The method constructs several multi-class support vector machine (SVM) classifiers, and generates the basic probability assignment (BPA) by the class-wise probability. Then D-S evidence theory is adopted to make the fusion and decision...
In this paper, we present a new approach which combines particle swarm optimization (PSO) with ensemble techniques to study credit risk assessment problems. In each iteration of the proposed method, PSO is used to solve feature subset selection problems and then nearest neighbor classifiers classify credit risk. Finally, all individual classification outputs are combined to generate the final aggregated...
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses...
Support vector machine (SVM) for pattern recognition is a binary classifier. When dealing with multi-class tasks, a popular and applicable way is to decompose the original problem into a set of binary sub-problems. This paper presents a novel half-versus-half (HVH) decomposition scheme. Unlike the conventional implementation methods, HVH is built via dividing the training dataset of K-classes into...
This paper presents a new approach to credit scoring by synthesizing simple nai??ve Bayesian classifier (SNBC) and the rough set theory. We adopted the combination of SNBC and rough set theory to build credit scoring model. The experiment was done on German Credit Database and showed that the model has a good prediction performance and has real world value upon application.
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