Using Over-sampling and Multi-classifier Committee Approach for skewed class distribution – a case study of diagnosis model construction of Benign prostate hypertrophy and Cancer of prostate

• Main Authors: Li Wei, 劉力瑋
• Other Authors: Fan Wu
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/78752271295978172163

碩士 === 國立中正大學 === 資訊管理所 === 95 === Regarding the non-skewed distribution, to utilize the existing data mining classification to construct the prediction model can reach a certain level of prediction accuracy. However, in the real data mining case, the dataset distribution is always skewed distribution. In clinical case, because the number of healthy people is more than the number of unhealthy people, the collected data would be congenital skewed distribution. If we utilize those dataset with skewed distribution to construct the prediction model, the prediction deviation should be a big problem. There are three existing solutions for skewed distribution – Under-sampling, Over-sampling, and Multi-classifier Committee Approach. This research will utilize Over-sampling and Multi-classifier Committee Approach for skewed distribution and improve them. The research objective is to raise the prediction accuracy of the minor part of the dataset. The case study is the disease of benign prostate hypertrophy and cancer of prostate. And this research will use those data to test the classification efficiency of my algorithm.