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Improving the Learning Accuracy of the Mega Trend Diffusion Technique for Small Data Sets

In the real world, collecting enough data to achieve an effective learning task is sometimes difficult for researchers. The learning results are thus often unsatisfactory, since most of machine learning algorithms are basically developed under the assumption that training sets can fully represent population properties. Once training sets do not contain enough population properties, knowledge could be biased. In the past few decades, virtual sample generation approaches have shown to be an effective way to improve the knowledge acquisition capability of small data sets. In this paper, a better version of mega-trend-diffusion technique is proposed to estimate a more proper domain ranges of small data sets. A data set related to bladder cancer is taken to validate the effectiveness of the proposed approach, and the experiments show better results.