Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions. Although data-driven methods have yielded notable successes across various benchmark datasets, they inherently treat models as opaque entities, thereby limiting their interpretability and yielding a lack of explanatory insights into their decision-making processes. In this work, we introduce Latent Boost, a novel approach that integrates advanced distance metric learning into supervised classification tasks, enhancing both interpretability and training efficiency. Thus during training, the model is not only optimized for classification metrics of the discrete data points but also adheres to the rule that the collective representation zones of each class should be sharply clustered. By leveraging the rich structural insights of intermediate model layer latent representations, Latent Boost improves classification interpretability, as demonstrated by higher Silhouette scores, while accelerating training convergence. These performance and latent structural benefits are achieved with minimum additional cost, making it broadly applicable across various datasets without requiring data-specific adjustments. Furthermore, Latent Boost introduces a new paradigm for aligning classification performance with improved model transparency to address the challenges of black-box models.

翻译：监督式机器学习通常遵循数据驱动范式，其内部模型参数通过自主优化使预测输出与真实值趋同，无需显式编程规则或先验假设。尽管数据驱动方法在各种基准数据集上取得了显著成功，但这些方法本质上将模型视为不透明实体，从而限制了其可解释性，并导致对其决策过程缺乏解释性洞见。本研究提出Latent Boost，一种将先进距离度量学习集成到监督分类任务中的新方法，以同时提升可解释性与训练效率。该方法在训练过程中不仅针对离散数据点的分类指标进行优化，同时遵循各类别的集体表征区域应紧密聚类的规则。通过利用中间模型层潜在表征的丰富结构信息，Latent Boost在提升轮廓系数的同时加速训练收敛，从而增强了分类可解释性。这些性能与潜在结构优势的实现在额外成本极小的条件下达成，使其无需针对特定数据调整即可广泛适用于各类数据集。此外，Latent Boost开创了一种将分类性能与提升模型透明度相协调的新范式，以应对黑箱模型带来的挑战。