Separation Failure in Linear Programming Discriminant Models
研究了线性规划判别分析模型中，当训练样本完全可分时，模型却选出不完全分离的判别函数（即分离失败）的问题，并发现通过将两组标签互换后重复应用模型可避免此问题。
Linear programming discriminant analysis (LPDA) models are designed around a variety of objective functions, each representing a different measure of separation of the training samples by the resulting discriminant function. A separation failure is defined to be the selection of an “optimal” discriminant function which incompletely separates a pair of completely separable training samples. Occurrence of a separation failure suggests that the chosen discriminant function may have an unnecessarily low classification accuracy on the actual populations involved. In this paper, a number of the LPDA models proposed for the two‐group case are examined to learn which are subject to separation failure. It appears that separation failure in any model can be avoided by applying the model twice, reversing group designations.