HiPaR: Hierarchical Pattern-Aided Regression
Abstract:
We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form p⇒ 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. 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 pbkp_rediction performance.
Año de publicación:
2021
Keywords:
- linear regression
- rule mining
Fuente:

Tipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Aprendizaje automático
- Algoritmo
- Algoritmo
Áreas temáticas:
- Métodos informáticos especiales
- Ingeniería y operaciones afines
- Programación informática, programas, datos, seguridad