In the literature, univariate uncertain data has a quantitative interval for each attribute in each transaction, which is accompanied by a probability density function indicating the probability that each value in the interval exists and appears. To the best of our knowledge, classifying univariate uncertain data has thus far seldom been addressed in the literature. Here, we propose the AssoU2Classifier algorithm to address this research gap. The AssoU2Classifier algorithm retrieves association rules from the univariate uncertain data to serve as a classification model. In addition, the U2Pruning procedure is developed to prune the association rules. The U2Pruning procedure not only reduces the number of association rules, which considerably accelerates the classification process, but also achieves high classification accuracies. In the experiments, the AssoU2Classifier algorithm was compared with 14 existing algorithms on 12 modified UCI datasets. The AssoU2Classifier algorithm obtained better classification accuracy than the compared algorithms on most of the datasets. Statistical tests (Friedman test and pairwise Wilcoxon test) also justified the advantage of the AssoU2Classifier algorithm. In addition, the AssoU2Classifier algorithm also had average learning time.

在文献中，单变量不确定数据为每个事务中的每个属性都有一个定量间隔，并附有一个概率密度函数，该函数指示该间隔中每个值存在和出现的概率。据我们所知，迄今为止，文献中很少涉及对单变量不确定数据进行分类。在这里，我们提出AssoU2Classifier算法来解决这一研究空白。AssoU2Classifier算法从单变量不确定数据中检索关联规则，以用作分类模型。此外，开发了U2Pruning程序以修剪关联规则。U2Pruning过程不仅减少了关联规则的数量，从而大大加快了分类过程，而且还实现了较高的分类精度。在实验中，在12个修改的UCI数据集上，将AssoU2Classifier算法与14种现有算法进行了比较。与大多数数据集上的比较算法相比，AssoU2Classifier算法获得了更好的分类精度。统计测试（Friedman检验和成对的Wilcoxon检验）也证明了AssoU2Classifier算法的优势。此外，AssoU2Classifier算法的学习时间也平均。