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Conference Papers Year : 2018

Attribute Coordinate Comprehensive Evaluation Model Combining Principal Component Analysis

(1) , (1) , (1)
1
Xiaolin Xu
  • Function : Author
  • PersonId : 1046603
Yan Liu
  • Function : Author
  • PersonId : 1046604
Jiali Feng
  • Function : Author
  • PersonId : 1046605

Abstract

Attribute coordinate comprehensive evaluation method features subjective weighting in which the weights of indicators are determined by evaluators, which possibly leads to the arbitrariness in setting the weights. When there are many indicators, it is difficult to accurately judge if the sample is better or worse than others. To address the problem, this paper applies principal component analysis on the attribute coordinate comprehensive evaluation method. When there are many indicators, they can be reduced to new indicators with related meanings given through the method of principal component analysis. With the simplification, it will greatly facilitate experts to rate samples, which is the paramount basis that provides the preference of experts for the attribute coordinate comprehensive evaluation method to further calculate all the satisfaction degrees of objects to be evaluated. Experimental results show the advantages of the improved algorithm over the original algorithm.
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Dates and versions

hal-02118804 , version 1 (03-05-2019)

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Attribution - CC BY 4.0

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Xiaolin Xu, Yan Liu, Jiali Feng. Attribute Coordinate Comprehensive Evaluation Model Combining Principal Component Analysis. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.60-69, ⟨10.1007/978-3-030-01313-4_7⟩. ⟨hal-02118804⟩
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