Aggregating Human-Expert Opinions for Multi-Label Classification
نویسندگان
چکیده
This paper introduces a multi-label classification problem to the field of human computation. The problem involves training data such that each instance belongs to a set of classes. The true class sets of all the instances are provided together with their estimations presented by m human experts. Given the training data and the class-set estimates of the m experts for a new instance, the multilabel classification problem is to estimate the true class set of that instance. To solve the problem we propose an ensemble approach. Experiments show that the approach can outperform the best expert and the majority vote of the experts.
منابع مشابه
Exploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملGeneralizing the conjunction rule for aggregating conflicting expert opinions
In multi-agent expert systems, the conjunction rule is commonly used to combine expert information represented by imprecise probabilities. However, it is well-known that this rule cannot be applied in case of expert conflict. In this paper, we propose to resolve expert conflict by means of a second-order imprecise probability model. The essential idea underlying the model is a notion of behavio...
متن کاملMutual Information-based multi-label feature selection using interaction information
Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than singlelabel feature selection tasks. In this paper, we propose the Mutual Information-based m...
متن کامل