This study develops a graph search algorithm to find the optimal
discrimination path for the binary classification problem. The objective
function is defined as the difference of variations between the true positive
(TP) and false positive (FP). It uses the depth first search (DFS) algorithm to
find the top-down paths for discrimination. It proposes a dynamic optimization
procedure to optimize TP at the upper levels and then reduce FP at the lower
levels. To accelerate computing speed with improving accuracy, it proposes a
reduced histogram algorithm with variable bin size instead of looping over all
data points, to find the feature threshold of discrimination. The algorithm is
applied on top of a Support Vector Machine (SVM) model for a binary
classification problem on whether a person is fit or unfit. It significantly
improves TP and reduces FP of the SVM results (e.g., reduced FP by 90% with a
loss of only\ 5% TP). The graph search auto-generates 39 ranked discrimination
paths within 9 seconds on an input of total 328,464 objects, using a dual-core
Laptop computer with a processor of 2.59 GHz.

本研究通过使用图搜索算法，针对二元分类问题提出了一种优化判别路径的算法。该算法使用深度优先搜索算法寻找自顶向下的判别路径，并应用于支持向量机模型，显著提高了真正例的分类准确性，同时减少了误正例。