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摘要:
In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.
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文献信息
篇名 Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains
来源期刊 电脑和通信(英文) 学科 医学
关键词 Quality Assessment SUBJECTIVE Domain MULTIMODAL Sensor Data LABEL Noise LIKELIHOOD ADJUSTING TCM Zheng
年,卷(期) 2017,(7) 所属期刊栏目
研究方向 页码范围 152-168
页数 17页 分类号 R73
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
Quality
Assessment
SUBJECTIVE
Domain
MULTIMODAL
Sensor
Data
LABEL
Noise
LIKELIHOOD
ADJUSTING
TCM
Zheng
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
电脑和通信(英文)
月刊
2327-5219
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
783
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0
总被引数(次)
0
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