[導讀]Learning from Imbalanced Classes

原文：Learning from Imbalanced Classes

數據不平衡是一個很是經典的問題，數據挖掘、計算廣告、NLP等工做常常遇到。該文總結了可能有效的方法，值得參考：

• Do nothing. Sometimes you get lucky and nothing needs to be done. You can train on the so-called natural (or stratified) distribution and sometimes it works without need for modification.
• Balance the training set in some way:
  • Oversample the minority class.
  • Undersample the majority class.
  • Synthesize new minority classes.
• Throw away minority examples and switch to an anomaly detection framework.
• At the algorithm level, or after it:
  • Adjust the class weight (misclassification costs).
  • Adjust the decision threshold.
  • Modify an existing algorithm to be more sensitive to rare classes.
• Construct an entirely new algorithm to perform well on imbalanced data.