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:

scopusscopus

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