基于模糊多标签AdaBoost算法的心脏瓣膜疾病分类
发布时间：2018-08-30 13:46
【摘要】：针对心脏瓣膜疾病模糊分类问题,提出基于多标签Ada Boost的模糊分类改进算法。结合模糊集理论,采用隶属函数将疾病的严重程度映射到区间[0,1]内的实数值,将超声诊断结果用模糊标签向量表示。利用余弦相似性分析疾病之间的复杂关系,计算标签相关性矩阵并对模糊标签向量进行补充。结合实际问题选取合适的阈值,将标签空间划分为标签集、标签相关集和标签无关集。本文算法以最小化排序损失为目标,针对不同的标签给予不同的权值调整因子,调整样本权重更新速度,强迫弱分类器关注与样本标签相关性较高的标签。在临床超声心动图(TTE)测量数据集上的实验结果表明:在对超声诊断结果模糊化时,通过隶属函数将疾病严重程度中的"无病"映射为0,"轻度"映射到区间[0.8,0.85],"中度"映射到区间[0.85,0.9],"重度"映射到区间[0.9,1],构造模糊标签矩阵,并通过标签相关性矩阵对其进行补充,此时所构造的分类器性能达到最优。将本文算法与Ada Boost.MLR算法、Ada Boost.MR算法、BPMLL算法、Rank SVM算法和ML-KNN算法进行对比分析,在多标签分类的5种评价指标上,本文算法的分类性能均优于其他对比算法,分类结果更接近超声诊断结果。
[Abstract]:An improved fuzzy classification algorithm based on multi-label Ada Boost is proposed for fuzzy classification of heart valve diseases. Based on the fuzzy set theory, the degree of disease severity is mapped to the real value in the interval by membership function, and the results of ultrasonic diagnosis are represented by fuzzy label vector. Using cosine similarity to analyze the complex relationship between diseases, the label correlation matrix is calculated and the fuzzy label vector is supplemented. The label space is divided into tag set, tag correlation set and label independent set. This algorithm aims at minimizing the ranking loss, gives different weight adjustment factors for different labels, adjusts the update speed of sample weights, and forces the weak classifier to pay attention to the labels with high correlation with the sample labels. The experimental results on the clinical echocardiographic (TTE) data set show that: when the results of ultrasonic diagnosis are blurred, The "disease-free" degree of disease severity is mapped to zero, "mild" to interval [0.88 0.85], "moderate" to interval [0.850.90], "heavy" to interval [0.99 ~ 1] by membership function, and fuzzy label matrix is constructed, which is supplemented by label correlation matrix. The performance of the proposed classifier is optimal. This paper compares this algorithm with that of Ada Boost.MLR algorithm, Ada Boost.MR algorithm and ML-KNN algorithm. The classification performance of this algorithm is better than that of other comparison algorithms on five evaluation indexes of multi-label classification. The classification results are closer to the ultrasonic diagnosis results.
【作者单位】： 中国科学院成都计算机应用研究所;中国科学院大学;
【基金】：四川省科技支撑计划资助项目(2016JZ0035) 中科院西部之光人才培养计划项目资助
【分类号】：R542.5;TP301.6
，
本文编号：2213260
[Abstract]:An improved fuzzy classification algorithm based on multi-label Ada Boost is proposed for fuzzy classification of heart valve diseases. Based on the fuzzy set theory, the degree of disease severity is mapped to the real value in the interval by membership function, and the results of ultrasonic diagnosis are represented by fuzzy label vector. Using cosine similarity to analyze the complex relationship between diseases, the label correlation matrix is calculated and the fuzzy label vector is supplemented. The label space is divided into tag set, tag correlation set and label independent set. This algorithm aims at minimizing the ranking loss, gives different weight adjustment factors for different labels, adjusts the update speed of sample weights, and forces the weak classifier to pay attention to the labels with high correlation with the sample labels. The experimental results on the clinical echocardiographic (TTE) data set show that: when the results of ultrasonic diagnosis are blurred, The "disease-free" degree of disease severity is mapped to zero, "mild" to interval [0.88 0.85], "moderate" to interval [0.850.90], "heavy" to interval [0.99 ~ 1] by membership function, and fuzzy label matrix is constructed, which is supplemented by label correlation matrix. The performance of the proposed classifier is optimal. This paper compares this algorithm with that of Ada Boost.MLR algorithm, Ada Boost.MR algorithm and ML-KNN algorithm. The classification performance of this algorithm is better than that of other comparison algorithms on five evaluation indexes of multi-label classification. The classification results are closer to the ultrasonic diagnosis results.
【作者单位】： 中国科学院成都计算机应用研究所;中国科学院大学;
【基金】：四川省科技支撑计划资助项目(2016JZ0035) 中科院西部之光人才培养计划项目资助
【分类号】：R542.5;TP301.6
，
本文编号：2213260
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