Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning
| العنوان: | Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning |
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| المؤلفون: | Aggarwal, A., Ghoshal, S., Ankith, M. S., Sinha, S., Ramakrishnan, G., Purushottam Kar, Jain, P. |
| المصدر: | Scopus-Elsevier |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence (AAAI), 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | General Medicine |
| الوصف: | The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is available. We present a novel method for optimizing multivariate performance measures in the MIML setting. Our approach MIML-perf uses a novel plug-in technique and offers a seamless way to optimize a vast variety of performance measures such as macro and micro-F measure, average precision, which are performance measures of choice in multi-label learning domains. MIML-perf offers two key benefits over the state of the art. Firstly, across a diverse range of benchmark tasks, ranging from relation extraction to text categorization and scene classification, MIML-perf offers superior performance as compared to state of the art methods designed specifically for these tasks. Secondly, MIML-perf operates with significantly reduced running times as compared to other methods, often by an order of magnitude or more. |
| تدمد: | 2374-3468 2159-5399 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c18dec7310e091b9640fff37ef5c5e08 https://doi.org/10.1609/aaai.v31i1.10947 |
| رقم الأكسشن: | edsair.doi.dedup.....c18dec7310e091b9640fff37ef5c5e08 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23743468 21595399 |
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