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New models of the attention-based random forests called LARF (LeafAttention-based Random Forest) are proposed. The first idea behind the modelsis 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. Thesecond level is the tree attention depending on the "leaf" attention. Thesecond idea is to replace the softmax operation in the attention with theweighted sum of the softmax operations with different parameters. It isimplemented by applying a mixture of the Huber's contamination models and canbe regarded as an analog of the multi-head attention with "heads" defined byselecting a value of the softmax parameter. Attention parameters are simplytrained by solving the quadratic optimization problem. To simplify the tuningprocess of the models, it is proposed to make the tuning contaminationparameters to be training and to compute them by solving the quadraticoptimization problem. Many numerical experiments with real datasets areperformed for studying LARFs. The code of proposed algorithms can be found inhttps://github.com/andruekonst/leaf-attention-forest.

LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models