Self-training credit evaluation integrated classification model based on data editing
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摘要: 针对信用数据不平衡及类标签数据难以获取的问题，提出一种基于数据剪辑的自训练信用评估集成分类模型。首先，采用合成少数类过采样法（SMOTE）在有标记样本上采样，以缓解数据不平衡性。其次，在少量带标签样本数据集上构建Stacking集成模型，并对无标记样本做“伪标记”，以获取类标签数据。最后，提出一种改进的双重加权半监督K近邻算法，并利用其剪辑伪标签数据和扩充训练集，直到模型收敛。使用UCI和Kaggle信用评估数据集进行仿真试验，结果表明，该模型具有更好的预测性能，更能有效识别少数类样本。
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关键词:
- 信用评估 /
- 半监督学习 /
- Stacking集成策略 /
- 数据剪辑 /
- 自训练
Abstract: Aiming at the problems of unbalance of credit data and difficult acquisition of label data, a self-training credit evaluation integrated classification model based on data editing was proposed. Firstly, synthetic minority over-sampling technique (SMOTE) was used to sample labeled samples to alleviate data imbalance. Secondly, a Stacking integration model was constructed on a few labeled sample datasets and unlabeled samples were "falsified" to obtain label-like data. Finally, an improved semi-supervised double-weighted K-nearest neighbor algorithm was proposed, which was used to clip the pseudo-label data and expand the training set until the model converged. Simulation experiments of UCI and Kaggle credit evaluation dataset show that the model has better predictive performance and can identify a few types of samples more effectively. -
图 1 基于SDWKNN的自训练信用评估流程图
Figure 1. Self-training credit evaluation glow chart based on SDWKNN
表 1 数据集概况
Table 1. Data set overview
数据集 样本量 特征数 类别 正样占比/% Taiwanese 4618 92 2 4.8 Financial 3670 83 2 3.7 
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表 2 各个阶段参数设置
Table 2. Parameter Settings of each phase
数据集 SMOTE 特征选择 p SDWKNN Taiwanese (0.2, 4) 40 10 (4, 6) Financial (0.3, 3) 40 10 (4, 6)
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表 4 各个模型G值对比
Table 4. Comparisons of G values of each model
编号 Taiwanese数据集 Financial数据集 3:2 1:1 1:2 1:3 1:4 均值 3:2 1:1 1:2 1:3 1:4 均值 1 0.699 0.682 0.632 0.620 0.602 0.647 0.518 0.520 0.487 0.522 0.487 0.507 2 0.664 0.696 0.671 0.629 0.609 0.654 0.491 0.480 0.519 0.499 0.459 0.490 3 0.683 0.669 0.670 0.626 0.626 0.655 0.503 0.483 0.436 0.492 0.468 0.476 4 0.664 0.680 0.680 0.641 0.658 0.665 0.708 0.713 0.707 0.707 0.707 0.708 5 0.679 0.645 0.658 0.670 0.659 0.662 0.537 0.474 0.534 0.559 0.542 0.529 6 0.706 0.678 0.709 0.697 0.688 0.696 0.707 0.713 0.718 0.718 0.718 0.715 7 0.712 0.730 0.753 0.736 0.748 0.736 0.707 0.718 0.718 0.717 0.718 0.715 8 0.726 0.724 0.759 0.748 0.756 0.743 0.707 0.718 0.718 0.718 0.718 0.716
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表 5 各个模型F值对比
Table 5. Comparisons of F values of each model
编号 Taiwanese数据集 Financial数据集 3:2 1:1 1:2 1:3 1:4 均值 3:2 1:1 1:2 1:3 1:4 均值 1 0.577 0.554 0.489 0.469 0.463 0.510 0.355 0.364 0.322 0.357 0.309 0.341 2 0.552 0.589 0.571 0.505 0.491 0.542 0.330 0.317 0.349 0.334 0.291 0.324 3 0.583 0.564 0.575 0.512 0.517 0.550 0.351 0.329 0.269 0.342 0.308 0.320 4 0.552 0.568 0.582 0.526 0.560 0.558 0.388 0.393 0.386 0.385 0.384 0.387 5 0.561 0.508 0.524 0.540 0.536 0.534 0.295 0.233 0.289 0.308 0.303 0.286 6 0.590 0.556 0.563 0.542 0.532 0.557 0.386 0.391 0.395 0.396 0.396 0.393 7 0.574 0.588 0.585 0.536 0.562 0.569 0.386 0.397 0.395 0.391 0.394 0.393 8 0.606 0.578 0.593 0.546 0.562 0.577 0.386 0.397 0.394 0.394 0.394 0.393
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