Yabacı Tak Ayşegül
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Bezmialem Vakıf University, Istanbul, Türkiye.
Sci Rep. 2023 Oct 13;13(1):17403. doi: 10.1038/s41598-023-44691-6.
There are several categorical effect size methods in the literature. It is not clear which method performs better for a given dataset and it is a challenging task to select the correct method for a given dataset. In this sense, to overcome the questions like "Which method should we choose?" and "Which categorical effect size method is more reliable for a given dataset?", an adaptive categorical effect size method based on intuitionistic meta fuzzy functions is introduced in the paper. Thus, the main motivation of the proposed method is to obtain more accurate outcomes by combining the results of better performing methods instead of relying on only one method. In the study, the intuitionistic fuzzy c-means clustering algorithm is adapted to meta fuzzy functions by incorporating not only membership degrees but also non-membership degrees to improve the clustering accuracy of meta fuzzy functions. Meta fuzzy functions are the linear combination of seven categorical effect size methods and the weights, which are calculated from membership grades from intuitionistic fuzzy c-means algorithm. Among the functions, the one with the lowest mean absolute percentage error is selected as the best. To evaluate the performance of the proposed method, 2 × 3, 2 × 4, and 3 × 4 contingency tables were simulated. Additionally, the performance of the proposed method is also assessed by applying it to a real-time dataset. Experimental results show that the proposed method outperforms compared to the evaluated seven categorical effect size methods in terms of mean absolute percentage error. Also, the calculated effect sizes are within the range of ±10% in terms of bias. Thus, the results verified that proposed method achieves greater reliability.
文献中有几种分类效应量方法。对于给定的数据集，尚不清楚哪种方法表现更好，为给定数据集选择正确的方法是一项具有挑战性的任务。从这个意义上说，为了克服诸如“我们应该选择哪种方法？”以及“对于给定数据集，哪种分类效应量方法更可靠？”等问题，本文引入了一种基于直觉元模糊函数的自适应分类效应量方法。因此，所提出方法的主要动机是通过组合表现较好的方法的结果来获得更准确的结果，而不是仅依赖于一种方法。在该研究中，直觉模糊 c 均值聚类算法不仅通过纳入隶属度，还通过纳入非隶属度来适应元模糊函数，以提高元模糊函数的聚类精度。元模糊函数是七种分类效应量方法与权重的线性组合，权重由直觉模糊 c 均值算法的隶属度计算得出。在这些函数中，选择平均绝对百分比误差最低的函数作为最佳函数。为了评估所提出方法的性能，模拟了 2×3、2×4 和 3×4 列联表。此外，还通过将所提出的方法应用于实时数据集来评估其性能。实验结果表明，在所提出的方法在平均绝对百分比误差方面优于所评估的七种分类效应量方法。而且，计算出的效应量在偏差方面在±10%的范围内。因此，结果验证了所提出的方法具有更高的可靠性。
