Active Learning for Multiclass Cost-sensitive Classification Using Probabilistic Models
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === Multiclass cost-sensitive active learning is a relatively new problem. In this thesis, we derive the maximum expected cost and cost-weighted minimum margin strategy for multiclass cost-sensitive active learning. These two strategies can be seem as the extended...
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| Format: | Others |
| Language: | en_US |
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2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/65244803215661729379 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === Multiclass cost-sensitive active learning is a relatively new problem. In this thesis, we derive the maximum expected cost and cost-weighted minimum margin strategy for multiclass cost-sensitive active learning. These two strategies can be seem as the extended version of classical cost-insensitive active learning strategies. The experimental results demonstrate that the derived strategies are promising for cost-sensitive active learning. In particular, the cost-sensitive strategies outperform cost-insensitive ones on many benchmark data sets. The results also reveal how the hardness of data affects the performance of active learning strategies. Thus, in practical active learning applications, data analysis before strategy selection can be important.
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