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MLCE: A Multi-Label Crotch Ensemble Method for Multi-Label Classification
International Journal of Pattern Recognition and Artificial Intelligence, 2020Multi-label classification addresses the problem that each instance is associated with multiple labels simultaneously. In this paper, we propose a multi-label crotch ensemble (MLCE) model for multi-label classification, which takes label correlations into consideration. In MLCE, a multi-label cluster tree is first constructed. Then, we incorporate all
Yuan Yao 0016 +3 more
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Discriminative Multi-label Model Reuse for Multi-label Learning
2020Traditional Chinese Medicine (TCM) with diagnosis scales is a holistic way for diagnosing Parkinson’s Disease, where symptoms can be represented as multiple labels. To solve this problem, multi-label learning provides a framework for handling such task and has exhibited excellent performance.
Yi Zhang 0073 +4 more
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Multi-label classification of feedbacks
Journal of Intelligent & Fuzzy Systems, 2021This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation ...
Dorian Ruiz Alonso +4 more
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Scalable multi-label annotation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014We study strategies for scalable multi-label annotation, or for efficiently acquiring multiple labels from humans for a collection of items. We propose an algorithm that exploits correlation, hierarchy, and sparsity of the label distribution. A case study of labeling 200 objects using 20,000 images demonstrates the effectiveness of our approach.
Jia Deng 0001 +5 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen 0001 +4 more
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Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen 0001 +4 more
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Multi-label Selective Ensemble
2015Multi-label selective ensemble deals with the problem of reducing the size of multi-label ensembles whilst keeping or improving the performance. In practice, it is of important value, since the generated ensembles are usually unnecessarily large, which leads to extra high computational and storage cost.
Nan Li 0019 +2 more
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Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012
Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approaches require the label relationship as prior knowledge, or exploit by counting the label co ...
Sheng-Jun Huang +2 more
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Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approaches require the label relationship as prior knowledge, or exploit by counting the label co ...
Sheng-Jun Huang +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2018
It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
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It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
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Interactive Multi-label Segmentation
2011This paper addresses the problem of interactive multilabel segmentation. We propose a powerful new framework using several color models and texture descriptors, Random Forest likelihood estimation as well as a multi-label Potts-model segmentation. We perform most of the calculations on the GPU and reach runtimes of less than two seconds, allowing for ...
Jakob Santner +2 more
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A Study on Multi-label Classification
2013Multi-label classifications exist in many real world applications. This paper empirically studies the performance of a variety of multi-label classification algorithms. Some of them are developed based on problem transformation. Some of them are developed based on adaption.
Clifford A. Tawiah, Victor S. Sheng
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