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A fuzzy decision tree can be constructed from a training set of cases and converted into a set of fuzzy rules. In this paper, the reasoning ability of four inductive operators, which are used for applying fuzzy rules to classification, are analyzed and compared. The purpose of this study is to show some useful guidelines on how to choose an appropriate operator for classified problem.
This paper is concerned with the fuzzy support vector classification, in which both of the type of the output training point and the value of the final fuzzy classification function are triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then, we transform this programming into its equivalence quadratic programming. Final, a fuzzy...
In machine learning classification, the classifier can be described by some rules, and the rules can be expressed by fuzzy granules corresponding to fuzzy concepts. In this paper we will introduce fuzzy information granulation to the process of building fuzzy classifier. Furthermore, we will present an optimized information granulation based machine learning classification algorithm. Experiments carried...
A sample and class incremental learning algorithm based on hyper-sphere support vector machine is proposed. For every class, hyper-sphere support vector machine is used to get the smallest hyper-sphere that contains most samples of the class, which can divide the class samples from others. In the process of incremental learning, the hyper-sphere of every new class are trained, and the history hyper-spherees...
Vector space model is used in most text categorization methods without considering the important information such as the order and co- occurrence of words within the text. In this paper we describe a novel approach of text classification using graph-based KNN. We reduce the number of features dimensions by a combined feature selection method. Then we present an improved graph-based text representation...
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