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IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
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Efficient use of local edge histogram descriptor
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Top-Down Induction of Clustering Trees
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Bounds on the Generalization Performance of Kernel Machine Ensembles
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Combining Pattern Classifiers: Methods and Algorithms
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International Journal of Computer Vision
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The CLEF 2005 Automatic Medical Image Annotation Task
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
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Pattern Recognition Letters
Decision trees for hierarchical multi-label classification
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Ensembles of Multi-Objective Decision Trees
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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A survey of hierarchical classification across different application domains
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PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
A genetic algorithm for Hierarchical Multi-Label Classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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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 time and these classes/labels are organized in a hierarchy. Our approach to HMC exploits the annotation hierarchy by building a single predictive clustering tree (PCT) that can simultaneously predict all annotations of an image. Hence, PCTs are very efficient: a single classifier is valid for the hierarchical semantics as a whole, as compared to other approaches that produce many classifiers, each valid just for one given class. To improve performance, we construct ensembles of PCTs. We evaluate our system on the IRMA database that consists of X-ray images. We investigate its performance under a variety of conditions. To begin with, we consider two ensemble approaches, bagging and random forests. Next, we use several state-of-the-art feature extraction approaches and combinations thereof. Finally, we employ two types of feature fusion, i.e., low and high level fusion. The experiments show that our system outperforms the best-performing approach from the literature (a collection of SVMs, each predicting one label at the lowest level of the hierarchy), both in terms of error and efficiency. This holds across a range of descriptors and descriptor combinations, regardless of the type of feature fusion used. To stress the generality of the proposed approach, we have also applied it for automatic annotation of a large number of consumer photos with multiple annotations organized in semantic hierarchy. The obtained results show that this approach is general and easily applicable in different domains, offering state-of-the-art performance.