C4.5: programs for machine learning
C4.5: programs for machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine Learning
Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Convex incremental extreme learning machine
Neurocomputing
Hybrid deformable model for aneurysm segmentation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Voting based extreme learning machine
Information Sciences: an International Journal
Knowledge management in image-based analysis of blood vessel structures
Knowledge and Information Systems
Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair
Computers in Biology and Medicine
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Robust extreme learning machine
Neurocomputing
Hybrid classifiers based on semantic data subspaces for two-level text categorization
International Journal of Hybrid Intelligent Systems
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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This paper introduces the Hybrid Extreme Rotation Forest HERF classifier describing two succesful applications in the image segmentation domain. The HERF is an ensemble of classifiers composed of Extreme Learning Machines ELM and Decision Trees. Training of the HERF includes optimal rotation of random partitions of the feature set aimed to increase diversity. The first application is the segmentation of 3D Computed Tomography Angiography CTA following an Active Learning AL strategy for the optimal sample selection to minimize the number of data samples needed to obtain a required accuracy degree. AL is pertinent for interactive learning processes where a human operator is required to select training samples to enhance the classifier in an iterative process, therefore labeling samples for training may be a time consuming and expensive process. CTA image segmentation is one of such processes, due to the variability in CTA images which hinders the generalization of classifiers trained on one dataset to new datasets. Following an AL strategy, the human operator is presented with a visual selection of pixels whose labeling would be most informative for the classifier. After adding those labeled pixels to the training data, the classifier is retrained. This iteration is repeated until image segmentation quality meets the required level. The approach is applied to the segmentation of the thrombus in CTA imaging of Abdominal Aortic Aneurysm AAA patients, showing that the structures of interest can be accurately segmented after a few iterations using a small data sample. The second application is a new semisupervised segmentation algorithm for hyperspectral images. The algorithm steps are: 1 supervised training an initial classifier from a small balanced training set, 2 clustering of the image pixels, by a k-means algorithm 3 adding unlabeled pixels to the original trainning data set according to the spatial neighborhood and the cluster membership, 4 supervised training of the classifier with the enriched training data set, 6 classification of the hyperspectral image 4 spatial regularization of classification results consisting in selecting the most frequent class in each pixel neighborhood. Results on two well known benchmarking hyperspectral images improve over state of the art algorithms.