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An extensive experimental comparison of methods for multi-label learning
Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In thisExpand
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Hierarchical annotation of medical images
We present a hierarchical multi-label classification (HMC) system for medical image annotation. HMC is a variant of classification where an instance may belong to multiple classes at the same timeExpand
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Ensembles of Multi-Objective Decision Trees
Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target attribute. Given theExpand
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Tree ensembles for predicting structured outputs
In this paper, we address the task of learning models for predicting structured outputs. We consider both global and local predictions of structured outputs, the former based on a single model thatExpand
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Predicting gene function using hierarchical multi-label decision tree ensembles
BackgroundS. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, toExpand
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Hierarchical classification of diatom images using ensembles of predictive clustering trees
Abstract This paper presents a hierarchical multi-label classification (HMC) system for diatom image classification. HMC is a variant of classification where an instance may belong to multipleExpand
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Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition
An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods toExpand
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Ensembles of Extremely Randomized Trees for Multi-target Regression
In this work, we address the task of learning ensembles of predictive models for predicting multiple continuous variables, i.e., multi-target regression (MTR). In contrast to standard regression,Expand
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Ensembles for Predicting Structured Outputs
While ensembles have been used for structured output learning, the literature lacks an extensive study of different strategies to construct ensembles in this context. In this work, we fill this gapExpand
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Potential of multi-objective models for risk-based mapping of the resilience characteristics of soils: demonstration at a national level
Policy makers rely on risk-based maps to make informed decisions on soil protection. Producing the maps, however, can often be confounded by a lack of data or appropriate methods to extrapolate usingExpand
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