This paper studies an application of indefinite proximity learning to the prediction of baskets of products of logs in the sawmill industry. More precisely, it focuses on the usage of the dissimilarity space embedding framework to generate a set of features representing wood logs. According to this framework, data points are represented by a vector of dissimilarity measures toward a set of representative data points named landmarks. This representation can then be used to train any of the large variety of available ML models requiring structured features. However, this framework raises the problem of selecting these landmarks. A new method is proposed to select these landmarks which is compared with four other methods from the literature. Numerical experiments are run to compare these methods on a dataset from the Canadian sawmill industry. The data representations obtained are used to train random forests and neural networks ensemble models. Results demonstrate that both the Partition Around Medoids (PAM) method and the newly proposed CoreSelect methods lead to a small but significant reduction in the mean square error of the predictions.