A credit risk evaluation model for telecom clients based on query-by-committee method of active learning
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摘要: 電信客戶信用風險等級評估是對電信客戶的信用風險進行等級分類.針對建立客戶信用風險等級分類模型時,大量帶有類標注數據難以獲得的問題,提出了基于主動學習的分類器建模方法,并對基于QBC(委員會投票選擇)的主動學習算法進行改進以提高分類器的預測精度.通過對實際電信客戶數據進行信用風險等級建模實驗,結果表明:應用新算法,分類器使用了較少的帶類標簽樣本數據,達到了與被動學習相同的精度,大大降低了信用專家評估數據的工作量.Abstract: Evaluating telecom clients' credit risk rate is classifying their credit risk level. An approach based on active learning was proposed for solving the insufficient labeled data problem in building a credit risk rate classifier. The new QBC (query-by-committee, QBC) method of active learning was presented to improve the classifier's accuracy. By applying the actual telecom clients data in the experiment, the results show that the model built by the new algorithm with less labeled training data can reach the same accuracy as passive learning. This can reduce annotation cost for credit evaluation experts.
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Key words:
- telecom clients /
- credit rating /
- active learning /
- vote /
- Kullback-Leibler divergence
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