Application of SVM and Fuzzy Set Theory for Classifying with Incomplete Survey Data
作者： Chao Lu , Xue-wei Li , Hong-bo Pan
DOI: 10.1109/ICSSSM.2007.4280164
关键词:
摘要: Classification with incomplete survey data is a new subject, and also which an important theme in mining. This paper proposes novel, powerful classification machine, support vector machine (SVM) based model of for data. Using this model, translated to fuzzy patterns without missing values firstly, then used these as the exemplar set teaching machine. Experimental results from real-world verify effectiveness applicability proposed model. Compared other techniques, method can utilize more information provided by data, reveal risk result.
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