Integrating cluster analysis with granular computing for imbalanced data classification problem – A case study on prostate cancer prognosis

2018 ◽  
Vol 125 ◽  
pp. 319-332 ◽  
Author(s):  
R.J. Kuo ◽  
P.Y. Su ◽  
Ferani E. Zulvia ◽  
C.C. Lin
Keyword(s):  
Prostate Cancer ◽  
Cluster Analysis ◽  
Granular Computing ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Cancer Prognosis ◽  
Imbalanced Data Classification ◽  
Prostate Cancer Prognosis

scholarly journals K-Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 2074-2079
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Classification Algorithms ◽  
Classification Problems ◽  
Imbalanced Classification ◽  
Imbalanced Data Classification ◽  
Traditional Classification ◽  
Boosting Method ◽  
Ensemble Algorithms

Imbalanced data classification is a critical and challenging problem in both data mining and machine learning. Imbalanced data classification problems present in many application areas like rare medical diagnosis, risk management, fault-detection, etc. The traditional classification algorithms yield poor results in imbalanced classification problems. In this paper, K-Means cluster based undersampling ensemble algorithm is proposed to solve the imbalanced data classification problem. The proposed method combines K-Means cluster based undersampling and boosting method. The experimental results show that the proposed algorithm outperforms the other sampling ensemble algorithms of previous studies.

scholarly journals K-Means Cluster Based Oversampling Algorithm for Imbalanced Data Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 3436-3440
Keyword(s):  
Machine Learning ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Skewed Data ◽  
Challenging Problem ◽  
Classification Problems ◽  
Imbalanced Data Classification

Imbalanced data classification problems endeavor to find a dependent variable in a skewed data distribution. Imbalanced data classification problems present in many application areas like, medical disease diagnosis, risk management, fault-detection, etc. It is a challenging problem in the field of machine learning and data mining. In this paper, K-Means cluster based oversampling algorithm is proposed to solve the imbalanced data classification problem. The experimental results show that the proposed algorithm outperforms the existing oversampling algorithms of previous studies.

scholarly journals A Hybrid Sampling SVM Approach to Imbalanced Data Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Qiang Wang
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Experimental Results ◽  
Training Dataset ◽  
Imbalanced Data Classification ◽  
Real World Datasets ◽  
Classification Information ◽  
Hybrid Sampling

Imbalanced datasets are frequently found in many real applications. Resampling is one of the effective solutions due to generating a relatively balanced class distribution. In this paper, a hybrid sampling SVM approach is proposed combining an oversampling technique and an undersampling technique for addressing the imbalanced data classification problem. The proposed approach first uses an undersampling technique to delete some samples of the majority class with less classification information and then applies an oversampling technique to gradually create some new positive samples. Thus, a balanced training dataset is generated to replace the original imbalanced training dataset. Finally, through experimental results on the real-world datasets, our proposed approach has the ability to identify informative samples and deal with the imbalanced data classification problem.

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