Filtering E-Mail Based on Fuzzy Support Vector Machines and Aggregation Operator

2006 ◽  
pp. 882-891 ◽  
Author(s):  
Jilin Yang ◽  
Hong Peng ◽  
Zheng Pei
Keyword(s):  
Support Vector Machines ◽  
Aggregation Operator ◽  
Support Vector ◽  
Vector Machines ◽  
Fuzzy Support Vector Machines ◽  
E Mail

Time-weighted Fuzzy Support Vector Machines for classification in changing environments

2021 ◽  
Vol 559 ◽  
pp. 97-110
Author(s):  
Sebastián Maldonado ◽  
Julio López ◽  
Carla Vairetti
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Changing Environments ◽  
Vector Machines ◽  
Fuzzy Support Vector Machines

scholarly journals Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Guang-Yih Sheu
Keyword(s):  
Support Vector Machines ◽  
Financial Distress ◽  
Fuzzy Variable ◽  
Financial Ratios ◽  
Misclassification Rate ◽  
Benford’S Law ◽  
Support Vector ◽  
Vector Machines ◽  
Benford's Law ◽  
Fuzzy Support Vector Machines

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

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