The size and configuration of pores are key features for automatic wood identification. In this paper, two feature sets insensitive to rotation and transition change are extracted and then used for construction of decision trees for recognizing three different kinds of pore distributions in wood microscopic images. The contribution of this paper lies in three aspects. Firstly, two direction ins...

Classification trees based on exhaustive search algorithms tend to be biased towards selecting variables that afford more splits. As a result, such trees should be interpreted with caution. This article presents an algorithm called QUEST that has negligible bias. Its split selection strategy shares similarities with the FACT method, but it yields binary splits and the final tree can be selected...

Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the num...

Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some subtrees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. We propose here a new pruning method (called pruning) which takes into account the complexity of sub-trees and which is able to kee...

Aimed at the problem of huge computation, large tree size and over-fitting of the testing data for multivariate decision tree (MDT) algorithms, we proposed a novel roughset-based multivariate decision trees (RSMDT) method. In this paper, the positive region degree of condition attributes with respect to decision attributes in rough set theory is used for selecting attributes in multivariate tes...

Linear discriminant analysis (LDA) is frequently used for classification/prediction problems in physical anthropology, but it is unusual to find examples where researchers consider the statistical limitations and assumptions required for this technique. In these instances, it is difficult to know whether the predictions are reliable. This paper considers a nonparametric alternative to predictiv...

This paper presents a new methodology for building decision trees or classification trees (Consolidated Trees Construction algorithm) that faces up the problem of unsteadiness appearing in the paradigm when small variations in the training set happen. As a consequence, the understanding of the made classification is not lost, making this technique different from techniques such as bagging and b...

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done usi...

A classification method based on deep representation of input data and ensembles of decision trees is introduced and evaluated solving the problem of vehicle classification and image classification with large number of categories.

For a set T of rooted binary leaf-labelled trees, we present an algorithm that finds all of the minor-minimal trees that are compatible with T . The running time of this algorithm is polynomial up to the number of trees with this property. This type of problem arises in several areas of classification, particularly evolutionary biology.