Mortality Prediction of ICU Patient Based on Imbalanced Data Classification Model

LISS2019 ◽  
2020 ◽  
pp. 83-94
Author(s):  
Xuedong Gao ◽  
Hailan Chen ◽  
Yifan Guo
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Mortality Prediction ◽  
Classification Model ◽  
Imbalanced Data Classification

scholarly journals A Novel Model for Imbalanced Data Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6680-6687
Author(s):  
Jian Yin ◽  
Chunjing Gan ◽  
Kaiqi Zhao ◽  
Xuan Lin ◽  
Zhe Quan ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Public Datasets ◽  
Distribution Cost ◽  
Novel Model ◽  
Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

Imbalanced Data Classification Algorithm Based on Clustering and SVM

2020 ◽  
pp. 2150036
Author(s):  
Bo Huang ◽  
Yimin Zhu ◽  
Zhongzhen Wang ◽  
Zhijun Fang
Keyword(s):  
Class Imbalance ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Algorithm ◽  
Classification Model ◽  
Imbalance Data ◽  
Imbalance Problem ◽  
Imbalanced Data Classification ◽  
Under Sampling ◽  
Feature Dimension

The class-imbalance learning is one of the most significant research topics in the data mining and machine learning. Imbalance problem means that one of the classes has much more samples than that of other classes. To deal with the issues of low classification accuracy and high time complexity, this paper proposes an novel imbalance data classification algorithm based on clustering and SVM. The algorithm suggests under-sampling in majority samples based on the distribution characteristics of minority samples. First, specific clusters are detected by cluster analysis on the minority. Second, a cluster boundary strategy is proposed to eliminate the bad influence of noise samples. To structure a balanced dataset for imbalance data, this paper proposes three principles of under-sampling on majority samples according to the characteristic of samples in the cluster. Finally, the optimal classification model from the linear combination of hybrid-kernel SVM is obtained. The experiments based on datasets in UCI and KEEL database show that our algorithm effectively decreases the interference of noise samples. Compared with the SMOTE and Fast-CBUS, the proposed algorithm not only reduces the feature dimension, but also improves the precision of the minor classes under the different labeled sample rates generally.

Data reduction and stacking for imbalanced data classification

2019 ◽  
Vol 37 (6) ◽  
pp. 7239-7249
Author(s):  
Ireneusz Czarnowski ◽  
Piotr Jędrzejowicz
Keyword(s):  
Data Reduction ◽  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification
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