5 accepted papers
In this work, we propose a novel approach that combines the strengths of FEAT and TabNet through knowledge distillation (KD), which we term FEAT-KD. FEAT is an intrinsically interpretable machine learning (ML) algorithm that constructs a weighted linear combination of concisely-represented features…
Symbolic Regression (SR) algorithms select expressions based on prediction performance while also keeping the expression lengths short to produce explainable white box models. In this context, SR algorithms can be evaluated by measuring the extent to which the expressions discovered are Pareto-optim…
Symbolic Regression (SR) is an approach which learns a closed-form function relating the predictors to the outcome in a dataset. Datasets are often multi-level (MuL), meaning that certain features can be used to split data into groups for analysis (we refer to these features as levels). The advantag…
We introduce a conceptually simple yet effective method to create small, compact decision trees - by using splits found via Symbolic Regression (SR). Traditional decision tree (DT) algorithms partition a dataset on axis-parallel splits. When the true boundaries are not along the feature axes, DT is…
Symbolic Regression (SR) is the well-studied problem of finding closed-form analytical expressions that describe the relationship between variables in a measurement dataset. In this paper, we rethink SR from two perspectives: morphology and adaptability. Morphology: Current SR algorithms typically u…