The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

Mahalanobis–Taguchi系统被认为是处理二进制分类案例的有前途且功能强大的工具。但是，马哈拉诺比斯-塔古奇（Mahalanobis-Taguchi）系统在筛选有用特征和以最佳方式确定决策边界方面存在一些限制。在本文中，提出了一个综合的Mahalanobis分类系统，该系统基于Mahalanobis距离及其空间的概念。集成的Mahalanobis分类系统将基于粒子群优化器的决策边界搜索过程直接集成到特征选择阶段，以构建Mahalanobis距离空间。这种集成（a）避免了依赖于用户的输入参数的需求，并且（b）提高了分类性能。在功能选择阶段，研究了二进制粒子群优化器和二进制重力搜索算法的使用。为了处理稀疏数据集时可能出现的过拟合问题，考虑k倍交叉验证。在分类性能方面，综合的Mahalanobis分类系统程序以经典的Mahalanobis–Taguchi系统以及最近提出的两阶段Mahalanobis分类系统为基准。在具有各种损伤类型的复杂形状金属涡轮叶片的实验案例研究以及具有蠕变和微结构损伤的圆柱状狗骨样品的综合案例研究中均给出了结果。结果表明，所提出的综合Mahalanobis分类系统显示出良好且鲁棒的分类性能。