La classificazione di cloni di pioppo con metodi montecarlo: le foreste casuali

Abstract: Tests for distinctness, homogeneity and stability of Poplar clones are still based on the use of proper descriptors of the main morphological and phenetic characteristics of the plant. Although the importance of a precise identification of clones is widely acknowledged, no sound technique has yet come into wide use. Many descriptors related to economic and productivity traits show a reduced repeatability or within clone variability. A multivariate approach by means of parametric procedure is ineffective due to the joint presence of variables with different sampling properties. We have applied some new numerical techniques based on computer simulation approaches to overcome difficulties due to the probability distribution of different traits. Among others, the procedure known as Random Forest was particularly suitable for clones discrimination. Random Forest, proposed by Leo Breiman (University of California, Berkeley,USA), is based on the building of a large set (Forest) of classification trees, generated at random, which are allowed to evolve generation by generation on the basis of computer simulations. Some internal estimates are produced and they are useful to describe the classification process and the relative importance of single traits. In the evaluation of 30 poplar clones by means of 18 descriptors, we received a good classification performance with a mean misclassification rate of 0,13 and with 22 clones with a rate under this value. The procedure allowed individuating the best variables according to classification ability. Final aim of the work is to individuate simple rules that can be easily applied in the typical condition of nursery