A Novel Classification Method Aided SAW
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Abstract
The Simple Additive Weighting (SAW) method is one of the easiest techniques for compensation multiple criteria decision making
(MCDM). This method used for solving multiple criteria decision problems. On the other hand, data mining is one of the 10 global sciencegrowing
and classification algorithms are the most important data mining method used for prediction. Most of these algorithms are complex and
time consuming. In this paper, we have proposed a new method to classify data using the SAW method aided Fisher Score for scoring the
features. The main advantages of this method are its simplicity, effectiveness and efficiency. We have used fuzzy functions for improving the
method accuracy. Based on experimental results on two Datasets, high accuracy is obtained comparing to the most classification algorithms.
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Keywords: Data mining; Classification; Simple Additive Weighting; Multiple criteria decision; Fisher Score; Fuzzy
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