论文标题
使用堆叠的概括结合各种学习者进行二进制分类
Combining Varied Learners for Binary Classification using Stacked Generalization
论文作者
论文摘要
与彼此相比，机器学习具有各种学习算法，在某些方面或其他方面都更好，但是所有算法都会遭受的常见错误是训练数据集非常高的训练数据。这通常最终将算法陷入耗尽性能的概括误差。可以使用称为堆叠的集合学习方法来解决这通常称为堆积的概括。在本文中，我们在高维多囊卵巢综合征数据集上使用堆叠的概括进行二进制分类，并证明模型变得概括并且指标显着改善。本文中给出了各种指标，该指标还指出了一种微妙的违法行为，发现接收器操作特征曲线被证明是不正确的。
The Machine Learning has various learning algorithms that are better in some or the other aspect when compared with each other but a common error that all algorithms will suffer from is training data with very high dimensional feature set. This usually ends up algorithms into generalization error that deplete the performance. This can be solved using an Ensemble Learning method known as Stacking commonly termed as Stacked Generalization. In this paper we perform binary classification using Stacked Generalization on high dimensional Polycystic Ovary Syndrome dataset and prove the point that model becomes generalized and metrics improve significantly. The various metrics are given in this paper that also point out a subtle transgression found with Receiver Operating Characteristic Curve that was proved to be incorrect.
