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Decomposition methods are multiclass classification schemes where the polychotomy is reduced into several dichotomies. Each dichotomy is addressed by a classifier trained on a training set derived from the original one on the basis of the decomposition rule adopted. These new training sets may present a disproportion between the classes, harming the global recognition accuracy. Indeed, traditional...
The Research of detection malware using machine learning method attracts much attention recent years. However, most of research focused on code analysis which is signature-based or analysis of system call sequence in Linux environment. Obviously, all methods have their strengths and weaknesses. In this paper, we concentrate on detection Trojan horse by operation system information in Windows environment...
Semi-supervised kernel learning is an important technique for classification and has been actively studied recently. In this paper, we propose a new semi-supervised spectral kernel learning method to learn a new kernel matrix with both labeled data and unlabeled data, which tunes the spectral of a standard kernel matrix by maximizing the margin between two classes. Our approach can be turned into...
Question classification plays a crucial important role in the question answering system. Recent research on question classification for open-domain mostly concentrates on using machine learning methods to resolve the special kind of text classification. This paper presents our research about Chinese question classification using machine learning method and gives our approach based on SVM and semantic...
The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different...
Sentiment classification aims at mining reviews of people for a certain event's topic or product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers' opinions from the web information. Automatic opinion mining will benefit...
In real worlds applications, some former research papers have shown that manifold learning tries to discover the non-linear low-dimensional data manifold from a high-dimensional space. Many natural images and the face images are believed to be sampled from a manifold. In this paper, we try to investigate whether discovering such manifold can aid the semi-supervised learning algorithms. We propose...
The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor generalization for the minority class instances. This paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and...
The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and the resulting learner. For this reason, many methods of automatic feature selection have been developed. By using the modularization of feature selection process, this paper evaluates a wide spectrum of these methods and some additional ones created by combination of different...
A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model...
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