Ensembles of decision trees have good prediction accuracy but suffer from a lack of interpretability. We propose a new approach for interpreting tree ensembles by finding prototypes in tree space, utilizing the naturally-learned similarity measure from the tree ensemble. Demonstrating the method on random forests, we show that the method benefits from two unique aspects of tree ensembles by lev...

the prediction of liquefaction potential of soil due to an earthquake is an essential task in civil engineering. the decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. c4.5 is a known algorithm widely used to design decision trees. in this algorithm, a pruning process is carried out to solve the problem of the...

Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have been recently proposed, with applications ranging from classification of web documents to bioinformatics. In this paper we propose a novel ensemble algorithm for multilabel, multi-path, tree-structured hierarchical classificati...

The random forest (RF) classifier is an ensemble classifier derived from decision tree idea. However the parallel operations of several classifiers along with use of randomness in sample and feature selection has made the random forest a very strong classifier with accuracy rates comparable to most of currently used classifiers. Although, the use of random forest on handwritten digits has been ...

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...

An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...

Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is...

This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy decision tree ensemble customized for each test instance. From the experimental results, we conclude that our boosting-style algorithm significantly improves the performance of the base learner. An empirical comparison...