Zhenxing Qin

Learn More
Many real-world data sets for machine learning and data mining contain missing values and much previous research regards it as a problem and attempts to impute missing values before training and testing. In this paper, we study this issue in cost-sensitive learning that considers both test costs and misclassification costs. If some attributes (tests) are(More)
We develop an innovative data preprocessing algorithm for classifying customers using unbalanced time series data. This problem is directly motivated by an application whose aim is to uncover the customers’ churning behavior in the telecommunication industry. We model this problem as a sequential classification problem, and present an effective solution for(More)
OBJECTIVES Obstructive sleep apnea often results in a wide range of comorbid conditions. Although some conditions have been clearly identified as comorbid, a full clinical pattern of associated diseases has not been systematically documented. This research aimed to reveal the full pattern of comorbid conditions associated with OSA by employing a data mining(More)
Many real-world datasets for machine learning and data mining contain missing values, and much previous research regards it as a problem, and attempts to impute missing values before training and testing. In this paper, we study this issue in cost-sensitive learning that considers both test costs and misclassification costs. If some attributes (tests) are(More)