  A new random forest based model for solving the Multiple Instance Learning
(MIL) problem under small tabular data, called Soft Tree Ensemble MIL
(STE-MIL), is proposed. A new type of soft decision trees is considered, which
is similar to the well-known soft oblique trees, but with a smaller number of
trainable parameters. In order to train the trees, it is proposed to convert
them into neural networks of a specific form, which approximate the tree
functions. It is also proposed to aggregate the instance and bag embeddings
(output vectors) by using the attention mechanism. The whole STE-MIL model,
including soft decision trees, neural networks, the attention mechanism and a
classifier, is trained in an end-to-end manner. Numerical experiments with
tabular datasets illustrate STE-MIL. The corresponding code implementing the
model is publicly available.
