Paper Info
Title
Mixed Integer Nonlinear Program for Minimization of Akaike's Information Criterion.
Abstract
Akaike's information criterion (AIC) is a measure of the quality of a statistical model for a given set of data. We can determine the best statistical model for a particular data set by the minimization based on the AIC. Since it is difficult to find the best statistical model from a set of candidates by this minimization in practice, stepwise methods, which are local search algorithms, are commonly used to find a better statistical model though it may not be the best. We formulate this AIC minimization as a mixed integer nonlinear programming problem and propose a method to find the best statistical model. In particular, we propose ways to find lower and upper bounds and a branching rule for this minimization. We then combine them with SCIP, which is a mathematical optimization software and a branch-and-bound framework. We show that the proposed method can provide the best statistical model based on AIC for small-sized or medium-sized benchmark data sets in UCI Machine Learning Repository. Furthermore, we show that this method can find good quality solutions for large-sized benchmark data sets.
Year
DOI
Venue
2016
10.1007/978-3-319-42432-3_36
Lecture Notes in Computer Science
Keywords
Field
DocType
Mixed integer nonlinear program,SCIP,Akaike's information criterion
Integer,Discrete mathematics,Mathematical optimization,Bayesian information criterion,Akaike information criterion,Nonlinear system,Computer science,Minification,Statistical model,Local search (optimization)
Conference
Volume
ISSN
Citations 
9725
0302-9743
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Keiji Kimura100.34
Hayato Waki237628.82