Pechenizkiy, M., Tsymbal, A., Puuronen, S. and Pechenizkiy, O. (2006) Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction. 19th IEEE International Symposium on Computer-Based Medical Systems, Salt Lake City, UT, 22-23 June 2006, 708-713.
https://doi.org/10.1109/cbms.2006.65

has been cited by the following article:

• TITLE: Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

  AUTHORS: Ying Dai

  KEYWORDS: Quality Assessment, Subjective Domain, Multimodal Sensor Data, Label Noise, Likelihood Adjusting, TCM Zheng

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.7, May 24, 2017

  ABSTRACT: 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.