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.
The work was supported by the Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University (No. XXKZD1604).
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Xu, X., Liu, Y., Feng, J. (2018). Attribute Coordinate Comprehensive Evaluation Model Combining Principal Component Analysis. In: Shi, Z., Pennartz, C., Huang, T. (eds) Intelligence Science II. ICIS 2018. IFIP Advances in Information and Communication Technology, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-01313-4_7
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DOI: https://doi.org/10.1007/978-3-030-01313-4_7
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