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Gradual Complex Numbers and Their Application for Performance Evaluation Classifiers
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DOI：10.1109/TFUZZ.2017.2688390.png)
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Abstract
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Usually, the evaluation of the classifiers performance is not an easy task to be performed, mainly when we analyze different criteria (output parameters). In this evaluation process, we can use quantitative measures (accuracy, specificity, among others), however, when the output values are very close and we have several criteria, the results are difficult to be interpreted by users. This paper aims to propose a new linguistic model to evaluate the performance of several classifiers. It is based on the notion of gradual complex numbers (GCN), proposed in [18]. In this paper, we present the theoretical basis of GCNs for classifier evaluator and we assess the performance of the proposed model (GCN) through an empirical study. In addition, the performance of GCN is compared with that of fuzzy complex numbers [6], and it reveals gains.
Keywords:
Computational intelligence
gradual complex numbers
gradual numbers
machine learning
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