期刊
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
卷 133, 期 -, 页码 10-22出版社
ELSEVIER
DOI: 10.1016/j.future.2022.02.022
关键词
Machine learning; Deep learning; Multi-class classification; Parameter optimization; Classification model training; Medical dataset; Radiological images recognition; Algorithm
资金
- Guangzhou Science and Technology Innovation and Development of Special Funds [EF003/FST-FSJ/2019/GSTIC, 201907010001, VCR 0000149]
In the medical domain, data collection and model optimization for multi-class models can be time-consuming and resource-intensive. This study introduces a novel strategy to achieve maximum accuracy while significantly reducing model training time, and preliminary experiments demonstrate the feasibility of this approach.
In the medical domain, data are often collected over time, evolving from simple to refined categories. The data and the underlying structures of the medical data as to how they have grown to today's complexity can be decomposed into crude forms when data collection starts. For instance, the cancer dataset is labeled either benign or malignant at its simplest or perhaps the earliest form. As medical knowledge advances and/or more data become available, the dataset progresses from binary class to multi-class, having more labels of sub-categories of the disease added. In machine learning, inducing a multi-class model requires more computational power. Model optimization is enforced over the multi-class models for the highest possible accuracy, which of course, is necessary for life-and-death decision making. This model optimization task consumes an extremely long model training time. In this paper, a novel strategy called Group-of-Single-Class prediction (GOSC) coupled with majority voting and model transfer is proposed for achieving maximum accuracy by using only a fraction of the model training time. The main advantage is the ability to achieve an optimized multi-class classification model that has the highest possible accuracy near to the absolute maximum, while the training time could be saved by up to 70%. Experiments on machine learning over liver dataset classification and deep learning over COVID19 lung CT images were tested. Preliminary results suggest the feasibility of this new approach.
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