Broad　learning　system　based　on　neural　network　(BLS-NN)　has　poor　efficiency　for　small　data　modeling　with　various　dimensions.　Tree-based　BLS　(TBLS)　is　designed　for　small　data　modeling　by　introducing　nondifferentiable　modules　and　an　ensemble　strategy　to　the　traditional　broad　learning　system　(BLS).　TBLS　replaces　the　neurons　of　BLS　with　the　tree　modules　to　map　the　input　data.　Moreover,　we　present　three　new　TBLS　variant　methods　and　their　incremental　learning　implementations,　which　are　motivated　by　deep,　broad,　and　ensemble　learning.　Their　major　distinction　is　reflected　in　the　incremental　learning　strategies　based　on:　1)　mean　square　error　(mse);　2)　pseudo-inverse;　and　3)　pseudo-inverse　theory　and　stack　representation.　Therefore,　this　study　further　explores　the　domain　of　BLS　based　on　the　nondifferentiable　modules.　The　simulations　are　compared　with　some　state-of-the-art　(SOTA)　BLS-NN　and　tree　methods　under　high-,　medium-,　and　low-dimensional　benchmark　datasets.　Results　show　that　the　proposed　method　outperforms　the　BLS-NN,　and　the　modeling　accuracy　is　remarkably　improved　with　the　small　training　data　of　the　proposed　TBLS.