Abstract

Machine learning methods for automatic classification problems using computational geometry are considered. Classes are defined by convex hulls of sets of points in a multidimensional feature space. The classification algorithms based on the evaluation of the proximity of a test point to the convex hulls of classes are examined. A new method for proximity evaluation based on linear programming is proposed. The corresponding nearest convex hull classifier is described. The results of experimental studies on real problems of medical diagnostics are presented. The comparison of the effectiveness of the proposed classifier with the classifiers of other types has shown a sufficiently high efficiency of the proposed method for proximity evaluation based on linear programming.

中文翻译：

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基于线性规划的最近凸包分类

摘要

考虑使用计算几何的自动分类问题的机器学习方法。类由多维特征空间中点集的凸包定义。检查基于测试点与类凸包的接近度评估的分类算法。提出了一种基于线性规划的邻近度评价新方法。描述了相应的最近凸包分类器。介绍了医学诊断实际问题的实验研究结果。所提出的分类器与其他类型分类器的有效性的比较表明，所提出的基于线性规划的邻近评估方法具有足够高的效率。