Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems
Identificadores
URI: https://hdl.handle.net/10481/77910Metadatos
Mostrar el registro completo del ítemEditorial
Springer
Materia
Inteligencia artificial Artificial intelligence
Fecha
2009Referencia bibliográfica
Guillén, A... [et al.] (2009). Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-04274-4_1]
Resumen
Pure feature selection, where variables are chosen or not to
be in the training data set, still remains as an unsolved problem, especially
when the dimensionality is high. Recently, the Forward-Backward
Search algorithm using the Delta Test to evaluate a possible solution was
presented, showing a good performance. However, due to the locality of
the search procedure, the initial starting point of the search becomes crucial
in order to obtain good results. This paper presents new heuristics to
find a more adequate starting point that could lead to a better solution.
The heuristic is based on the sorting of the variables using the Mutual
Information criterion, and then performing parallel local searches. These
local searches provide an initial starting point for the actual parallel
Forward-Backward algorithm.