Latent Dirichlet Allocation、classification、feature scaling、positive and negative data

• Main Authors: Shih-Yi Kuo, 郭士毅
• Other Authors: Jian-hua Yeh
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/59282352751313706118

碩士 === 真理大學 === 資訊工程學系碩士班 === 99 === Among various classification problems, the one-class problem focuses on how to correctly classify data into target class. The others outside target class are called outliers. In this paper, we are going to discuss the classification for extremely biased data set the amount of outliers is far larger than target set of data, and the outliers are discretely distributed. We use KDD Cup 2008 challenge as our target experiment. There are 1,712 patients, only 118 of them are malignant of breast cancer. Each patent’s data is represented as multiple candidates records. In summary, there are 102,294 candidates and only 623 positive (positive) samples. These positive samples are our target set for classification. Under the above data distribution, how to separate normal data with malignant data is a pretty difficult job. In this paper, we propose a classification process flow to solve this problem.