HiPaR: Hierarchical Pattern-aided Regression
This addresses the need for interpretable and accurate regression models in data analysis, though it appears incremental as it builds on existing pattern-based regression methods.
The paper tackles the problem of explaining tabular data with both categorical and numerical attributes by introducing HiPaR, a pattern-aided regression method that mines hybrid rules combining data region characterizations with local linear models, resulting in fewer rules than existing methods while achieving state-of-the-art prediction performance.
We introduce HiPaR, a novel pattern-aided regression method for tabular data containing both categorical and numerical attributes. HiPaR mines hybrid rules of the form $p \Rightarrow y = f(X)$ where $p$ is the characterization of a data region and $f(X)$ is a linear regression model on a variable of interest $y$. HiPaR relies on pattern mining techniques to identify regions of the data where the target variable can be accurately explained via local linear models. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.