Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (8): 2173-2181.doi: 10.23940/ijpe.19.08.p17.21732181
Previous Articles Next Articles
Wenjie Li*
Submitted on
;
Contact:
* E-mail address: About author:
Wenjie Li graduated from Northeast University and Yanshan University, China with a bachelor's degree and master's degree in engineering, respectively. She is currently a lecturer at Hebei Vocational & Technical College of Building Materials. Her research interests include graphic image processing and social network analysis.
Supported by:
Wenjie Li. Imbalanced Data Optimization Combining K-Means and SMOTE [J]. Int J Performability Eng, 2019, 15(8): 2173-2181.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
[1] Q. Jing, X. Z. Qian,W. T. Wang, “A Parallel Random Forest Algorithm for Imbalanced Big Data,” [2] L. Xue and S. W. Zhang, “Imbalanced Data Classification Algorithm based on Quadratic Random Forest,” [3] R. F. Chang, W. J. Wu,W. K. Moon, “Support Vector Machines for Diagnosis of Breast Tumors on US Images,” [4] Y. Shi, X. M. Li,X. H. Qi, “Classification Research of SVM with Imbalanced Data based on a New Type of under Sampling Samples,” [5] P. K.Chan and S. J. Stolfo, “Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection,” in [6] G. L. Sun, S. Li, Y. Cao,F. Lang, “Cervical Cancer Diagnosis based on Random Forest,” [7] N. V. Chawla, K. W. Bowyer,L. O. Hall, “SMOTE: Synthetic Minority over-Sampling Technique,” [8] H. Han, W. Y. Wang,B. H. Mao, “Borderline-SMOTE: A New over-Sampling Method in Imbalanced Data Sets Learning,” in [9] Y. J.Dong and X. H. Wang, “A New Over-Sampling Approach: Random-SMOTE for Learning from Imbalanced Data Sets,” [10] X. C. Wang, Z. M. Pan,L. L. Dong, “Research on Classification for Imbalanced Dataset based on Improved SMOTE,” [11] P. Thanathamathee and C. Lursinsap, “Handling Imbalanced Data Sets with Synthetic Boundary Data Generation using Bootstrap Re-Sampling and Adaboost Techniques,” [12] P. Vorraboot, S. Rasmequan,K. Chinnasarn, “Improving Classification Rate Constrained to Imbalanced Data Between Overlapped and Non-Overlapped Regions by Hybrid Algorithms,” [13] X. F. Li, J. Li, Y. F. Dong,C. W. Qu, “A New Learning Algorithm for Imbalanced Data-Pcboost,” [14] J. Yun, J. Ha,J. S. Lee, “Automatic Determination of Neighborhood Size in Smote,” in [15] W. Qiong, Y. T. Li,X. W. Zheng, “Optimization of Random Forest Algorithm for Classification of Imbalanced Training Sets,” [16] D. Devi, S. K. Biswas,B. Purkayastha, “Redundancy-Driven Modified Tomek-Link based undersampling: A Solution to Class Imbalance,” [17] W. Xue, “Improvement SMOTE Resampling Algorithm of Imbalanced Data Sets,” [18] W. Fan, S. J. Stolfo,J. X. Zhang, “Adacost: Misclassification Cost-Sensitive Boosting,” in [19] X. L.Wang and J. L. Wang, “Improving Adaboost Algorithm based on Cost-Sensitive,” [20] F. Y. Cheng, J. Zhang,C. H. Wen, “Cost-Sensitive Large Margin Distribution Machine for Classification of Imbalanced Data,” [21] S. Datta and S. Das, “Near-Bayesian Support Vector Machines for Imbalanced Data Classification with Equal or Unequal Misclassification Costs,” [22] J. Du, “Cost-Sensitive Learning and Its Application,” China University of Geosciences Doctoral Dissertation, Wuhan, China, December 2009 [23] C. Seiffert, T. M. Khoshgoftaar,J. VanHulse, “Rusboost: A Hybrid Approach to Alleviating Class Imbalance,” in [24] M. Galar, A. Fernandez,E. Barrenechea, “Ordering-based Pruning for Improving the Performance of Ensembles of Classifiers in the Framework of Imbalanced Data Sets,” [25] M. J. Kim, D. K. Kang,B. K. Hong, “Geometric Mean based Boosting Algorithm with over-Sampling to Resolve Data Imbalance Problem for Bankruptcy Prediction,” [26] X. S. Hu, J. P. Wen,Y. Zhong, “Imbalanced Data Ensemble Classification using Dynamic Balance Sampling,” [27] B. Scholkopf, J. C. Platt,J. Shawetaylor, “Estimating the Support of a High-Dimensional Distribution,” [28] C. Y. Wang, “Research on Classification Method of Imbalanced Data Sets and Its Application in Telecom Industry,” Zhejiang University Master Thesis, Hang Zhou, China, June 2011 |
[1] | Ashu Mehta, Navdeep Kaur, and Amandeep Kaur. A Review of Software Fault Prediction Techniques in Class Imbalance Scenarios [J]. Int J Performability Eng, 2025, 21(3): 123-130. |
[2] | Ashu Mehta, Navdeep Kaur, and Amandeep Kaur. Addressing Class Imbalance in Software Fault Prediction using BVPC-SENN: A Hybrid Ensemble Approach [J]. Int J Performability Eng, 2025, 21(2): 94-103. |
[3] | Sanjay M, Deepashree P. Vaideeswar, Kalapraveen Bagadi, Visalakshi Annepu, and Beebi Naseeba. Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy [J]. Int J Performability Eng, 2024, 20(5): 263-270. |
[4] | Vikas Verma, Arun Malik, and Isha Batra. Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach [J]. Int J Performability Eng, 2024, 20(5): 312-318. |
[5] | Manu Jyoti Gupta and Parveen Sehgal. Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm [J]. Int J Performability Eng, 2024, 20(3): 177-185. |
[6] | Ovais Bashir Gashroo and Monica Mehrotra. DetectHATE: Detecting Targeted Hate - A Framework for Classifying Online Abuse on X [J]. Int J Performability Eng, 2024, 20(11): 699-711. |
[7] | Janarthanan Sekar and Ganesh Kumar T. Hyperparameter Tuning in Deep Learning-Based Image Classification to Improve Accuracy using Adam Optimization [J]. Int J Performability Eng, 2023, 19(9): 579-586. |
[8] | Aashita Rajput, Muskan Yadav, Sachin Yadav, Megha Chhabra, and Arun Prakash Agarwal. Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning [J]. Int J Performability Eng, 2023, 19(9): 607-623. |
[9] | C. Rohith Bhat and Madhusundar Nelson. Artificial Intelligence Based Credit Card Fraud Detection for Online Transactions Optimized with Sparrow Search Algorithm [J]. Int J Performability Eng, 2023, 19(9): 624-632. |
[10] | Deepak Kumar, Chaman Verma, Purushottam Sharma, Deeksha Kumari, and Zoltán Illés. Demographic and Clinical Factors Role Identification in Stroke Risk and Subtype Prediction [J]. Int J Performability Eng, 2023, 19(6): 368-378. |
[11] | Rakesh Kumar, Sunny Arora, Ashima Arya, Neha Kohli, Vaishali Arya, and Ekta Singh. Ensemble Learning for Appraising English Text Readability using Gompertz Function [J]. Int J Performability Eng, 2023, 19(6): 388-396. |
[12] | Pranshu Kumar Soni and Leema Nelson. PCP: Profit-Driven Churn Prediction using Machine Learning Techniques in Banking Sector [J]. Int J Performability Eng, 2023, 19(5): 303-311. |
[13] | Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183. |
[14] | Harshita Batra and Leema Nelson. DCADS: Data-Driven Computer Aided Diagnostic System using Machine Learning Techniques for Polycystic Ovary Syndrome [J]. Int J Performability Eng, 2023, 19(3): 193-202. |
[15] | Shalaka Prasad Deore. SongRec: A Facial Expression Recognition System for Song Recommendation using CNN [J]. Int J Performability Eng, 2023, 19(2): 115-121. |
|