Monk-1

该数据集名为Monk-1，专为基于规则的符号机器学习算法设计。它包含6个符号特征，其中训练集有124个案例，测试集有432个案例。值得注意的是，邻近算法通常在这个数据集上的表现并不理想。该数据集的规模为124个训练案例和432个测试案例，其任务是进行符号机器学习的模型选择过程。This dataset, named Monk-1, is specifically designed for rule-based symbolic machine learning algorithms. It contains six symbolic features, with 124 training instances and 432 test instances. Notably, the k-nearest neighbors (k-NN) algorithm generally achieves subpar performance on this dataset. Its core task is to support symbolic machine learning model selection procedures.