Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

1BAbstract This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was to identify the grazed areas from Quickbird imagery to map their spatial extent. Seve...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

This paper presents an ensemble learning approach for classifying masses in mammograms as malignant or benign by using Breast Image Report and Data System (BI-RADS) descriptors. We first identify the most important BI-RADS descriptors based on the information gain measure. Then we quantize the fine-grained categories of those descriptors into coarse-grained categories. Finally we apply an ensem...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks...

Le travail présenté dans ce papier se place dans le contexte d’intégration de nouvelles méthodes de classification basée sur l’Analyse de Concepts Formels sous WEKA et propose une nouvelle famille nommée “Lattice” qui contient six méthodes basées toutes sur la construction d’un treillis de concepts. Le but de la classification est d’organiser un ensemble d’objets en classes homogènes. Les objet...