title: CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.
abstract: BACKGROUND
  The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization.
  
  
  RESULTS
  We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability.
  
  
  CONCLUSIONS
  The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.
authors.affiliation: School of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
authors.forename: Li
authors.initials: L
authors.lastname: Ma
authors.affiliation: School of Information Science and Technology, Jinan University, Guangzhou, 510632, China. tfsh@jnu.edu.cn.
authors.forename: Suohai
authors.identifier: http://orcid.org/0000-0002-4211-9726
authors.initials: S
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D016000:Cluster Analysis; D012372:ROC Curve
keywords: Feature selection; Imbalance data; Intelligence algorithm; Parameter optimization; Random forests