title: Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD.
abstract: Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). The difficulty of FPR lies in the variation of the appearances of the nodules, and the imbalance distribution between the nodule and non-nodule class. Moreover, the presence of inherent complex structures in data distribution, such as within-class imbalance and high-dimensionality are other critical factors of decreasing classification performance. To solve these challenges, we proposed a hybrid probabilistic sampling combined with diverse random subspace ensemble. Experimental results demonstrate the effectiveness of the proposed method in terms of geometric mean (G-mean) and area under the ROC curve (AUC) compared with commonly used methods.
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China; Computing Science, University of Alberta, Edmonton, Alberta, Canada. Electronic address: pcao1@ualberta.ca.
authors.forename: Peng
authors.initials: P
authors.lastname: Cao
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China. Electronic address: yangjinzhu@neusoft.com.
authors.forename: Jinzhu
authors.initials: J
authors.lastname: Yang
authors.affiliation: Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China. Electronic address: l-w@neusoft.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China. Electronic address: zhaodz@neusoft.com.
authors.forename: Dazhe
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Computing Science, University of Alberta, Edmonton, Alberta, Canada. Electronic address: zaiane@cs.ualberta.ca.
authors.forename: Osmar
authors.initials: O
authors.lastname: Zaiane
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Ensemble classifier; False positive reduction; Imbalanced data learning; Lung nodule detection; Random subspace method; Re-sampling