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用于阿尔茨海默症分类的模糊逻辑特征选择和异质集成学习方法

韩亮 杨婷 蒲秀娟 黄谦

韩亮, 杨婷, 蒲秀娟, 黄谦. 用于阿尔茨海默症分类的模糊逻辑特征选择和异质集成学习方法[J]. 电子与信息学报. doi: 10.11999/JEIT200963
引用本文: 韩亮, 杨婷, 蒲秀娟, 黄谦. 用于阿尔茨海默症分类的模糊逻辑特征选择和异质集成学习方法[J]. 电子与信息学报. doi: 10.11999/JEIT200963
Liang HAN, Ting YANG, Xiujuan PU, Qian HUANG. Method on Alzheimer’s Disease Classification Utilizing Fuzzy Logic Feature Selection and Heterogeneous Ensemble Learning[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT200963
Citation: Liang HAN, Ting YANG, Xiujuan PU, Qian HUANG. Method on Alzheimer’s Disease Classification Utilizing Fuzzy Logic Feature Selection and Heterogeneous Ensemble Learning[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT200963

用于阿尔茨海默症分类的模糊逻辑特征选择和异质集成学习方法

doi: 10.11999/JEIT200963
基金项目: 重庆市自然科学基金(cstc2016jcyjA0376)
详细信息
    作者简介:

    韩亮:男,1975年生,副教授,博士,研究方向为信号处理和图像处理

    杨婷:女,1996年生,硕士生,研究方向为生物医学信号处理

    蒲秀娟:女,1979年生,讲师,博士,研究方向为生物医学信号处理

    黄谦:男,1998年生,硕士生,研究方向为信号与信息处理

    通讯作者:

    韩亮 hanliangaa@cqu.edu.cn

  • 中图分类号: TN911.7

Method on Alzheimer’s Disease Classification Utilizing Fuzzy Logic Feature Selection and Heterogeneous Ensemble Learning

Funds: The Natural Science Foundation of Chongqing (cstc2016jcyjA0376)
  • 摘要: 阿尔茨海默症(AD)分类有助于在AD早期阶段及时采取针对性的治疗和干预措施,对降低老年群体的AD发病率和延缓AD疾病进展具有重要意义。该文提出一种改进的高斯模糊逻辑特征选择方法,首先采用互信息量和方差齐性分析两种方法给出特征重要性评分并分别进行归一化,然后使用改进的高斯模糊逻辑方法对其加权得到最终的特征重要性评分,最后依据特征重要性评分选取特征。该文还使用逻辑回归、随机森林、LightGBM、支持向量机和深度前馈网络作为初级分类器,多项式朴素贝叶斯分类器作为次级分类器,构建异质集成分类器,利用选取的特征进行AD分类。在TADPOLE数据集上进行实验,实验结果证实了所提特征选择方法是有效的,且采用所提特征选择方法,基于多项式朴素贝叶斯的异质集成分类器在AD分类上的性能要优于传统分类器。
  • 图  1  多项式朴素贝叶斯融合异质集成分类器

    图  2  不同特征数量占比下累计特征重要性评分与分类精度

    图  3  不同融合策略对比实验

    图  4  不同分类方法对比实验

    图  5  特征选择方法对比实验1

    图  6  特征选择方法对比实验2

    表  1  特征重要性评分排序前10的特征

    序号特征标签特征说明特征重要性评分
    1Hippocampus海马体(hippocampus)体积0.0437339
    2ST29SV左海马体(left hippocampus)体积0.0423217
    3ST88SV右海马体(right hippocampus)体积0.0410883
    4Entorhinal内嗅皮层(entorhinal)体积0.0375120
    5MidTemp中颞叶(Midtemp)体积0.0304293
    6ST123CV右侧未定义区域(皮层分割)体积0.0302178
    7ST64CV左侧未定义区域(皮层分割)体积0.0300861
    8ST24CV左内嗅皮层(left entorhinal)体积0.0296456
    9ST12SV左杏仁核(left amygdala)体积0.0286703
    10ST40CV左中颞叶(left middle temporal)体积0.0281707
    下载: 导出CSV

    表  2  分类器时间复杂度分析

    分类器训练所用时间(s)
    LR0.0468
    RF0.6396
    SVM0.3744
    LGB0.6084
    DFN1.2948
    MultinomialNB0.0001
    多项式朴素贝叶斯融合异质集成分类器2.9641
    下载: 导出CSV
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    CHU Zheng and YU Jiong. Performance prediction based on random forest for the stream processing checkpoint[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1452–1459. doi: 10.11999/JEIT190552
    钱亚冠, 卢红波, 纪守领, 等. 基于粒子群优化的对抗样本生成算法[J]. 电子与信息学报, 2019, 41(7): 1658–1665. doi: 10.11999/JEIT180777

    QIAN Yaguan, LU Hongbo, JI Shouling, et al. Adversarial example generation based on particle swarm optimization[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1658–1665. doi: 10.11999/JEIT180777
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    MARINESCU R V, OXTOBY N P, YOUNG A L, et al. TADPOLE challenge: Prediction of longitudinal evolution in Alzheimer’s disease[J]. arXiv: 1805.03909v2, 2018.
    ADNI. Cross-sectional FreeSurfer (6.0)[EB/OL]. https://adni.bitbucket.io/reference/ucsffsx6.html, 2020.
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出版历程
  • 收稿日期:  2020-11-10
  • 修回日期:  2021-01-31
  • 网络出版日期:  2021-03-01

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