We present a new classification algorithm for machine learning numerical data based on direct and inverse fuzzy transforms. In our previous work fuzzy transforms were used for numerical attribute dependency in data analysis: the multi-dimensional inverse fuzzy transform was used to approximate the regression function. Also here the classification method presented is based on this operator. Strictly speaking, we apply the K-fold cross-validation algorithm for controlling the presence of over-fitting and for estimating the accuracy of the classification model: for each training (resp., testing) subset an iteration process evaluates the best fuzzy partitions of the inputs. Finally, a weighted mean of the multi-dimensional inverse fuzzy transforms calculated for each training subset (resp., testing) is used for data classification. We compare this algorithm on well-known datasets with other five classification methods.

我们提出了一种新的基于直接和逆模糊变换的机器学习数值数据分类算法。在我们之前的工作中，模糊变换用于数据分析中的数值属性依赖性：多维逆模糊变换用于逼近回归函数。同样，这里介绍的分类方法也是基于这个算子的。严格来说，我们应用 K 折交叉验证算法来控制过度拟合的存在和估计分类模型的准确性：对于每个训练（或测试）子集，迭代过程评估输入。最后，为每个训练子集（分别是测试）计算的多维逆模糊变换的加权平均值用于数据分类。