Computer Science – Learning
Scientific paper
2007-09-21
Chemometrics and Intelligent Laboratory Systems / I Mathematical Background Chemometrics Intell Lab Syst 80, 2 (2006) 215-226
Computer Science
Learning
Scientific paper
10.1016/j.chemolab.2005.06.010
Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of parameters, leading to overfitting and poor generalization abilities. In this paper, we suggest the use of the mutual information measure to select variables from the initial set. The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is thus suitable for nonlinear modelling. In addition, it leads to the selection of variables among the initial set, and not to linear or nonlinear combinations of them. Without decreasing the model performances compared to other variable projection methods, it allows therefore a greater interpretability of the results.
François Damien
Lendasse Amaury
Rossi Fabrice
Verleysen Michel
Wertz Vincent
No associations
LandOfFree
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Mutual information for the selection of relevant variables in spectrometric nonlinear modelling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Mutual information for the selection of relevant variables in spectrometric nonlinear modelling will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-256795