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http://dx.doi.org/10.9723/jksiis.2022.27.4.019

Data Augmentation using a Kernel Density Estimation for Motion Recognition Applications  

Jung, Woosoon (대구대학교 정보통신공학과)
Lee, Hyung Gyu (덕성여자대학교 소프트웨어학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.4, 2022 , pp. 19-27 More about this Journal
Abstract
In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.
Keywords
Machine Learning; Kernel Density Estimation; Data Augmentation;
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