基于SMOTE和gcForest的医疗小样本数据分类研究

刘文昌; 魏赟; 袁浩轩; 高跃

doi:10.11959/j.issn.2096-3750.2023.00337

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基于SMOTE和gcForest的医疗小样本数据分类研究

理论与技术 | 更新时间：2024-08-16

- 基于SMOTE和gcForest的医疗小样本数据分类研究
- Research on medical small sample data classification based on SMOTE and gcForest
- 物联网学报 2023年7卷第2期页码：76-87
- 作者机构：
  
  1. 上海理工大学光电信息与计算机工程学院，上海 200093
  2. 复旦大学计算机科学技术学院，上海 200438
- 作者简介：
  
  [ "刘文昌（1998- ），男，上海理工大学光电信息与计算机工程学院硕士生，主要研究方向为机器学习、数据分类等" ]
  [ "魏赟（1976- ），女，博士，上海理工大学副教授，主要研究方向为分布式系统、网络信息控制等" ]
  [ "袁浩轩（1996- ），男，复旦大学计算机科学技术学院博士生，主要研究方向为深度学习、智能频谱感知等" ]
  [ "高跃（1978- ），男，博士，复旦大学教授，主要研究方向为卫星互联网、天空地一体化网络、压缩感知与机器学习、智能天线" ]
- 基金信息：
  
  国家重点研发计划;The National Key Research and Development Program of China(2018YFB1700902);国家发展和改革委员会资助的“基于5G网络特大型城市区域智慧医疗应急救援体系建设”项目
- DOI：10.11959/j.issn.2096-3750.2023.00337
  中图分类号： TP391
- 纸质出版日期：2023-06-30，
  
  网络出版日期：2023-06，
- 稿件说明：
移动端阅览
刘文昌, 魏赟, 袁浩轩, 等. 基于SMOTE和gcForest的医疗小样本数据分类研究[J]. 物联网学报, 2023,7(2):76-87.

WENCHANG LIU, YUN WEI, HAOXUAN YUAN, et al. Research on medical small sample data classification based on SMOTE and gcForest. [J]. Chinese journal on internet of things, 2023, 7(2): 76-87.
刘文昌, 魏赟, 袁浩轩, 等. 基于SMOTE和gcForest的医疗小样本数据分类研究[J]. 物联网学报, 2023,7(2):76-87. DOI： 10.11959/j.issn.2096-3750.2023.00337.

WENCHANG LIU, YUN WEI, HAOXUAN YUAN, et al. Research on medical small sample data classification based on SMOTE and gcForest. [J]. Chinese journal on internet of things, 2023, 7(2): 76-87. DOI： 10.11959/j.issn.2096-3750.2023.00337.

摘要

针对传统机器学习模型在医疗小样本数据上由浅层模型结构和复杂数据特征导致的分类表现不佳的问题，提出了一种联合多粒度改进级联森林（cgicForest，combine multi-grained improved cascade forest）模型。通过在多粒度扫描中加入随机抽样环节以及对变换特征进行优化来提高模型表征学习能力，并改进级联森林部分的层级结构来提升模型分类能力。针对存在类别不平衡问题的数据集，提出安全边界过采样（SBS

safe-bo

rderline-SMOTE）算法在属于安全边界的少数样本周围进行动态插值，提高训练数据质量，再通过cgicForest模型进行训练学习，最终得到支持不平衡医疗小样本数据的SBS-cgicForest分类模型。在3种医疗数据集上应用SBS-cgicForest分类模型进行测试，结果表明，cgicForest模型在具有复杂特征的医疗小样本数据上分类的性能指标较多粒度级联森林（gcForest

multi-grained cascade forest）模型提升了4.1～5.4个百分点，与SBS算法结合后各性能指标提升6.6～11.2个百分点，比与传统采样方法结合后的F

评分高出2～2.5个百分点，为解决医疗小样本数据的分类问题提供了参考，并为智慧医疗场景下的物联网应用提供了支持。

Abstract

Aiming at the problem of poor classification performance in traditional machine learning models caused by shallow model structure and complex data characteristics in small medical sample data

an combine multi- grained improved cascade forest (cgicForest) model was proposed.It enhances the representation learning ability of the model by adding random sampling into the multi-grained scanning and optimizing the transformation features.It also enhances the model's classification ability by updating the cascade forest’s hierarchical structure.Considering category imbalance problems in datasets

the safe-borderline-SMOTE (SBS) algorithm was proposed to dynamic interpolate around the few class samples belonging to the safety boundary

which can improve the quality of training data.The cgicForest was applied for training and learning

thus the SBS-cgicForest classification model was obtained which can support imbalanced medical small samples data.The model is used on three medical datasets for classification experiments.The results show that the performance indexes of the cgicForest model in the classification of medical small sample data with complex characteristics have increased by 4.1～5.4 percentage points

compared with the multi-grained cascade forest (gcForest) model.The performance indexes have increase by 6.6～11.2 percentage points after the combination with SBS algorithm

the F

score was 2～2.5 percentage points higher than that obtained by traditional sampling methods.It provides a reference for solving the classification problem of small medical sample da

and includes support for internet of things applications in smart medical scenarios.

关键词

医疗数据小样本SMOTEgcForest

Keywords

medical datasmall sampleSMOTEgcForest

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