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。

一种带有动态优化和减少直方图的快速图搜索算法用于二元分类问题辨别

A Fast Graph Search Algorithm with Dynamic Optimization and Reduced  Histogram for Discrimination of Binary Classification Problem

In this work, we introduce a new graph search algorithm, lazy edged based A*
(LEA*), for robot motion planning. By using an edge queue and exploiting the
idea of lazy search, LEA* is optimally vertex efficient similar to A*, and has
improved edge efficiency compared to A*. LEA* is simple and easy to implement
with minimum modification to A*, resulting in a very small overhead compared to
previous lazy search algorithms. We also explore the effect of inflated
heuristics, which results in the weighted LEA* (wLEA*). We show that the edge
efficiency of wLEA* becomes close to LazySP and, thus is near-optimal. We test
LEA* and wLEA* on 2D planning problems and planning of a 7-DOF manipulator. We
perform a thorough comparison with previous algorithms by considering sparse,
medium, and cluttered random worlds and small, medium, and large graph sizes.
Our results show that LEA* and wLEA* are the fastest algorithms to find the
plan compared to previous algorithms.

本研究提出了一种新的图搜索算法，即基于惰性边的 A*（LEA*），用于机器人运动规划，通过使用边队列和利用惰性搜索的思想，LEA * 在顶点上有效地与 A * 类似，并且相对于 A * 来说边的效率有所提高。LEA * 实现简单且易于操作，对 A * 进行最小修改，与之前的惰性搜索算法相比具有非常小的开销。我们还研究了膨胀启发函数的效果，得到加权 LEA*（wLEA*）。实验结果表明，wLEA * 的边效率接近于 LazySP，即近似最优。我们在 2D 规划问题和 7-DOF 机械臂规划中测试了 LEA * 和 wLEA*，并与之前的算法进行了全面比较，在稀疏、中等和复杂的随机环境以及小、中和大规模图中，结果显示 LEA * 和 wLEA * 是找到规划路径最快的算法。