# Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
## Appendix A
### Feature Selection Algorithm
#### a.2.2 Ig
Conversely, if the distance between \(R\), near hit and a feature is greater than the distance between \(R\) and near miss, indicating that the feature has a negative effect on distinguishing the nearest neighbors of the same class and different class, the weight of the feature is decreased. The above process is repeated \(m\) times, and finally, the average weight of each feature is obtained. The greater the weight of the feature is, the stronger the classification ability of the feature and the weaker the ability to classify the feature. The running time of the Relief algorithm increases linearly with the sampling number of samples \(m\) and the number of original features \(N\), so the operating efficiency is very high. Algorithm A4 introduces the specific steps. ## References

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