Using Wavelet Support Vector Machines to Generate Expected Outputs

Authors:
Mao Ye; Boqin Feng; Li Zhu; Yao Lin
Affiliations:
Sch. of Electron.&Inf. Eng., Xi'an Jiaotong Univ.;Sch. of Electron.&Inf. Eng., Xi'an Jiaotong Univ.;-;-
Venue:
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
Year:
2006

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Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts

Expert Systems with Applications: An International Journal

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Abstract

A test case consists of a set of inputs and a list of expected outputs. To automatically generate the expected outputs for the test case is rather difficult. An approach based on wavelet support vector machines (WSVM) is proposed to overcome it. After training, WSVM is used to automatically generate the expected outputs, which approximate the correct outputs. Actual outputs of the application under testing (AUT) are then compared with expected outputs in an interval to determine if there is a failure. Experiment and comparison show that the method is effective and can save a lot of time and labor in software testing