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.

GWO Optimized K-Means Cluster based Oversampling Algorithm

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Santha Subbulaxmi S ◽  
Arumugam G
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Classification Problem ◽  
The Other ◽  
Classification Algorithms ◽  
Skewed Data ◽  
Grey Wolf ◽  
Imbalanced Data Classification ◽  
Traditional Classification ◽  
Real World Applications

Skewed data distribution prevails in many real world applications. The skewedness is due to imbalance in the class distribution and it deteriorates the performance of the traditional classification algorithms. In this paper, we provide a Grey wolf optimized K-Means cluster based oversampling algorithm to handle the skewedness and solve the imbalanced data classification problem. Experiments are conducted on the proposed algorithm and compared it with the benchmarking popular algorithms. The results reveal that the proposed algorithm outperforms the other benchmarking algorithms.

Imbalanced Data Classification Based on Clustering

2013 ◽  
Vol 443 ◽  
pp. 741-745
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han ◽  
Rong Ze Xia
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Data Classification ◽  
Comprehensive Analysis ◽  
Classification Method ◽  
Classification Methods ◽  
Real World Data ◽  
Minority Class ◽  
Imbalanced Data Classification ◽  
Traditional Classification

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

scholarly journals Comprehensive Assessment of Imbalanced Data Classification

2020 ◽  
Vol 9 (4) ◽  
pp. 1426-1431
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Predictive Modelling ◽  
Classification Problem ◽  
Minority Class ◽  
Unequal Distribution ◽  
Imbalanced Classification ◽  
Imbalanced Data Classification ◽  
Improved Performance ◽  
And Performance

This is an attempt to address the various challenges opportunities and scope for formulating and designing new procedure in imbalanced classification problem which poses a challenge to a predictive modelling as many of AI ML n DL algorithms which are extensively used for classification are always designed from the perspective of with majority of focus on assuming equal number of examples for a class. It leads to poor efficiency and performance especially in minority class. As Minority class is always very crucial and sensitive to classification errors and also its utmost important in imbalanced classification. This chapter discusses addresses and gives novel as well as deep insights with unequal distribution of classes in training datasets. Largely real time and real world classifications are comprising imbalanced distribution so need specialized techniques for more challenging and sophisticated models with minimal errors and improved performance.

scholarly journals Multi-label Problem Transformation Methods: a Case Study

2011 ◽  
Vol 14 (1) ◽  
Author(s):  
Everton Alvares Cherman ◽  
Maria Carolina Monard ◽  
Jean Metz
Keyword(s):  
Classification Problem ◽  
Learning Problems ◽  
Classification Algorithms ◽  
Classification Problems ◽  
Practical Applications ◽  
Problem Transformation ◽  
Textual Data ◽  
Traditional Classification ◽  
Transformation Methods

Traditional classification algorithms consider learning problems that contain only one label, i.e., each example is associated with one single nominal target variable characterizing its property. However, the number of practical applications involving data with multiple target variables has increased. To learn from this sort of data, multi-label classification algorithms should be used. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. In this work, two well known methods based on this approach are used, as well as a third method we propose to overcome some deficiencies of one of them, in a case study using textual data related to medical findings, which were structured using the bag-of-words approach. The experimental study using these three methods shows an improvement on the results obtained by our proposed multi-label classification method.

Sign in / Sign up

Export Citation Format

Share Document