Quality classification of wood boards is an essential task in the sawmill
industry, which is still usually performed by human operators in small to
median companies in developing countries. Machine learning algorithms have been
successfully employed to investigate the problem, offering a more affordable
alternative compared to other solutions. However, such approaches usually
present some drawbacks regarding the proper selection of their hyperparameters.
Moreover, the models are susceptible to the features extracted from wood board
images, which influence the induction of the model and, consequently, its
generalization power. Therefore, in this paper, we investigate the problem of
simultaneously tuning the hyperparameters of an artificial neural network (ANN)
as well as selecting a subset of characteristics that better describes the wood
board quality. Experiments were conducted over a private dataset composed of
images obtained from a sawmill industry and described using different feature
descriptors. The predictive performance of the model was compared against five
baseline methods as well as a random search, performing either ANN
hyperparameter tuning and feature selection. Experimental results suggest that
hyperparameters should be adjusted according to the feature set, or the
features should be selected considering the hyperparameter values. In summary,
the best predictive performance, i.e., a balanced accuracy of $0.80$, was
achieved in two distinct scenarios: (i) performing only feature selection, and
(ii) performing both tasks concomitantly. Thus, we suggest that at least one of
the two approaches should be considered in the context of industrial
applications.

通过调整神经网络的超参数及选择更好地描述木板质量的特征来同时解决特征选择和超参数调整问题，对于工业应用来说，至少其中一种方法应被考虑。