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mi-DS: Multiple-Instance Learning Algorithm
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AI
DOI：10.1109/TSMCB.2012.2201468.png)
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摘要
En 中文
Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.
Keyword:
Multiple-instance learning (MIL)
rule-based algorithms
supervised learning
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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