title: Learning to improve medical decision making from imbalanced data without a priori cost.
abstract: BACKGROUND
  In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in the data. To deal with this problem, people often use a priori cost to adjust the learning process in the pursuit of optimal classification function. However, this priori cost is often unknown and hard to estimate in medical decision making.
  
  
  METHODS
  In this paper, we propose a new learning method, named RankCost, to classify imbalanced medical data without using a priori cost. Instead of focusing on improving the class-prediction accuracy, RankCost is to maximize the difference between the minority class and the majority class by using a scoring function, which translates the imbalanced classification problem into a partial ranking problem. The scoring function is learned via a non-parametric boosting algorithm.
  
  
  RESULTS
  We compare RankCost to several representative approaches on four medical data sets varying in size, imbalanced ratio, and dimension. The experimental results demonstrate that unlike the currently available methods that often perform unevenly with different priori costs, RankCost shows comparable performance in a consistent manner.
  
  
  CONCLUSIONS
  It is a challenging task to learn an effective classification model based on imbalanced data in medical data analysis. The traditional approaches often use a priori cost to adjust the learning of the classification function. This work presents a novel approach, namely RankCost, for learning from medical imbalanced data sets without using a priori cost. The experimental results indicate that RankCost performs very well in imbalanced data classification and can be a useful method in real-world applications of medical decision making.
authors.affiliation: Department of Computer Science and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong. xwan@comp.hkbu.edu.hk.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wan
authors.affiliation: Department of Computer Science and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong. jiming@comp.hkbu.edu.hk.
authors.forename: Jiming
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong. william@comp.hkbu.edu.hk.
authors.forename: William K
authors.initials: WK
authors.lastname: Cheung
authors.affiliation: Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong. tongt@comp.hkbu.edu.hk.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Tong
mesh_terms: D001943:Breast Neoplasms / Q000145:classification; D002965:Classification / Q000379:methods; D035061:Control Groups; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003657:Decision Making; D003920:Diabetes Mellitus / Q000145:classification; D005067:Euthyroid Sick Syndromes / Q000145:classification; D005260:Female; D006505:Hepatitis / Q000145:classification; D006801:Humans; D010361:Patients / Q000145:classification*; D015983:Selection Bias