论文标题
LARF：与污染模型混合的两级基于注意的随机森林
LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models
论文作者
论文摘要
提出了基于注意力的随机森林的新模型，称为LARF（基于叶子注意的随机森林）。模型背后的第一个想法是引入两级注意，其中之一是“叶子”注意力，并且注意力机制应用于树木的每一叶。第二层是树的注意，具体取决于“叶子”的注意力。第二个想法是用使用不同参数的软马克斯操作的加权总和替换注意力的softmax操作。它是通过应用Huber污染模型的混合物来实现的，可以被视为通过选择SoftMax参数的值来定义的“头”的多头注意的类似物。注意参数仅通过解决二次优化问题来训练。为了简化模型的调整过程，建议使调谐污染参数进行训练，并通过解决二次优化问题来计算它们。进行实际数据集的许多数值实验都用于研究LARF。可以在https://github.com/andruekonst/leaf-citchention-forest中找到建议的算法代码。
New models of the attention-based random forests called LARF (Leaf Attention-based Random Forest) are proposed. The first idea behind the models is to introduce a two-level attention, where one of the levels is the "leaf" attention and the attention mechanism is applied to every leaf of trees. The second level is the tree attention depending on the "leaf" attention. The second idea is to replace the softmax operation in the attention with the weighted sum of the softmax operations with different parameters. It is implemented by applying a mixture of the Huber's contamination models and can be regarded as an analog of the multi-head attention with "heads" defined by selecting a value of the softmax parameter. Attention parameters are simply trained by solving the quadratic optimization problem. To simplify the tuning process of the models, it is proposed to make the tuning contamination parameters to be training and to compute them by solving the quadratic optimization problem. Many numerical experiments with real datasets are performed for studying LARFs. The code of proposed algorithms can be found in https://github.com/andruekonst/leaf-attention-forest.
