论文标题
空间效率模型的概率价值选择
Probabilistic Value Selection for Space Efficient Model
论文作者
论文摘要
提出了当前主流预处理方法的替代方法：值选择（VS）。与现有方法（例如要消除了消除实例的特征和实例选择）的特征选择不同，值选择消除了数据集中的值（相对于每个功能），具有两个目的：降低模型大小并保留其精度。提出了基于信息理论指标的两种概率方法：PVS和P + VS。详细阐述了具有各种尺寸的基准数据集上的大量实验。将这些结果与现有的预处理方法（例如特征选择，特征转换和实例选择方法）进行了比较。实验结果表明，价值选择可以达到准确性和模型尺寸降低之间的平衡。
An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and P + VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results show that value selection can achieve the balance between accuracy and model size reduction.
