论文标题
带有卷积的树木的过渡矩阵表示
Transition Matrix Representation of Trees with Transposed Convolutions
论文作者
论文摘要
我们如何有效地在树模型中找到最佳结构？在域中，树模型比可解释性对于做出不可逆转的决策至关重要的域中的复杂黑框模型受到青睐。但是，寻找在性能和可解释性之间提供最佳平衡的树结构仍然是一项艰巨的任务。在本文中，我们提出了蛋t（带有转移卷积的过渡矩阵表示），这是我们新颖的广义树表示，以实现最佳结构搜索。 TART代表了一个树模型，具有一系列的转移卷积，可通过避免过渡矩阵的创建来提高推理的速度。结果，TART允许一个人搜索具有一些设计参数的最佳树结构，比基于功能数据集中的基线模型的分类精度更高。
How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making irreversible decisions. However, searching for a tree structure that gives the best balance between the performance and the interpretability remains a challenging task. In this paper, we propose TART (Transition Matrix Representation with Transposed Convolutions), our novel generalized tree representation for optimal structural search. TART represents a tree model with a series of transposed convolutions that boost the speed of inference by avoiding the creation of transition matrices. As a result, TART allows one to search for the best tree structure with a few design parameters, achieving higher classification accuracy than those of baseline models in feature-based datasets.
