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To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and trains SVMs on the decomposed regions. Although there are other means of decomposing a data space, we show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own means, thereby reducing the cost of SVM training...
In this paper, we propose a tree-structured multi-class classifier to identify annotations and overlapping text from machine printed documents. Each node of the tree-structured classifier is a binary weak learner. Unlike normal decision tree(DT) which only considers a subset of training data at each node and is susceptible to over-fitting, we boost the tree using all training data at each node with...
In privacy preserving classification, when data is stored in a centralized database and distorted using a randomization-based technique, we have information loss and reduced accuracy of classification. This paper presents a new approach to privacy preserving classification for centralized data based on Emerging Patterns. The presented solution gives higher accuracy of classification than a decision...
In this study, an article recommendation system for English reading comprehension improvement is proposed. The goal of this study is to find out the most important attributes that affect the difficulty of an article according to the levels granted by the General English Proficiency Test (GEPT). Using the determined attributes to classify the articles gathered by the crawler from the Internet everyday...
The decision tree is an important classification method in data mining classification. Aiming at deficiency of ID3 algorism, a new improved classification algorism is proposed in this paper. The new algorithm combines principle of Taylor formula with information entropy solution of ID3 algorism, and simplifies the information entropy solution of ID3 algorithm, then assigns a weight value N to simplified...
With the rapid increase of the worldpsilas population, the drastic changes in worldpsilas food supply, and the limitation of land resources, the pressure for agriculture is greater than ever before. With the development of AI theories and technologies, the study in the classification of agriculture data becomes more advanced and intellectualized. This paper mainly discusses a specific decision tree...
Network traffic classification plays an important role in various network activities. Due to the ineffectiveness of traditional port-based and payload-based methods, recent works proposed using machine learning methods to classify flows based on statistical characteristics. In this study, we evaluate the effectiveness of machine learning techniques on the real-time traffic classification problem....
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression,...
Support vector machine (SVM) is originally developed for binary classification problems. In order to solve practical multi-class problems, various approaches such as one-against-rest (1-a-r), one-against-one (1-a-1) and decision trees based SVM have been presented. The disadvantages of the existing methods of SVM multi-class classification are analyzed and compared in this paper, such as 1-a-r is...
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