Constructing a new virtual sample generation technique for small dataset learning

• Main Authors: Wei-ShanLing, 凌偉珊
• Other Authors: Der-Chiang Li
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/74105744105429620295

碩士 === 國立成功大學 === 工業與資訊管理學系碩士在職專班 === 104 === Since the rise of Generation Network, big data has become the hottest topic issue even small data recently. It is difficult to do further analysis and prediction due to small data is not easy to obtain and high cost. Virtual sample generation method proved an effective way to solve small data problem. The main technique is Mega-trend diffusion (MTD) that defined database on status of uniform distribution and skewness. These studies propose a non-parametric multi-modal virtual sample generation for multi-modal population. After running data preprocess, it will capture the maximum and useful data by using soft DBSCAN cluster method. Using estimated data range by MTD Algorithm and generate virtual sample for prediction.