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This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised...
Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in Forests can be replaced by even simpler structures, e.g., Random Naive Bayes classifiers, yielding similar performance. The goal of this paper is to benefit from these findings to develop an efficient on-line learner. Based...
Ensemble learning is a powerful machine learning paradigm which has exhibited apparent advantages in many applications. An ensemble in the context of machine learning can be broadly defined as a machine learning system that is constructed with a set of individual models working in parallel and whose outputs are combined with a decision fusion strategy to produce a single answer for a given problem...
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. But bagging does not work very well in some case, such as k-nearest neighbor (kNN). At the same time, query learning strategies using bagging is also not work very well. From features view, we introduce bagging features active learning (ALBF) for kNN and apply...
A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model...
In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting,...
In this paper, we propose a novel method for incremental semi-supervised learning. Unlike the traditional way of incremental learning or semi-supervised learning, we try to answer a more challenging question: given inadequate labeled training data, can one use the unlabeled testing data to improve the learning and prediction accuracy? The objective here is to reinforce the learning system trained...
Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the...
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