Since multi-label active learning becomes a hot topic, it is more challenging to train efficient and secure classification models, and reduce the label cost in the field of IIoT. To address this issue, this research focuses on the secure multi-label active learning for IIoT using an ...
In multilabel learning, the joint set of binary classification tasks is expressed with label binary indicator array: each sample is one row of a 2d array of shape (n_samples, n_classes) with binary values: the one, i.e. the non zero elements, corresponds to the subset of labels. An a...
To minimize the human-labeling efforts, we propose a novel multi-label active learning appproach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restric...
On Active Learning in Multi-label Classification In conventional multiclass classification learning, we seek to induce a prediction function from the domain of input patterns to a mutually exclusive set of class labels. As a straightforward generalization of this category of learning p... K Brinker...
active learning method based on SVM's expect margin which relies on current classifier,select samples that can reduce classifier's margin fastest.The experimental results show that the method based on expect margin outperforms than other active learning strategy based on decision value and posterior ...
Keywords: Multi-label,Posterior probability,Expect margin,Active learning,SVM多标签,后验概率,期望间隔,主动学习,支持向量机 Full-Text Cite this paper Add to My Lib Abstract: Classification is one of the key techniques of data mining. It requires a large number of training samples to oblain ...
In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becom
Brinker K. On Active Learning in Multi-label Classification[M]. Berlin, Germany: Springer, 2006.Klaus Brinker. 2006. On active learning in multi- label classification. In From Data and Information Analysis to Knowledge Engineering, pages 206- 213. Springer-Verlag....
O. Reyes, C. Morell, and S. Ventura, "Effective active learning strategy for multi-label learning," Neurocompu- ting, vol. 273, pp. 494-508, 2018.Effective active learning strategy for multi-label learning. OSCAR R,CARLOS M,SEBASTI N V. Neurocomputing . 2018...
Active learningrefers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabeled examples in terms of how...DOI: 10.1007/978-3-642-00958-7_12 被...