Data Mining Based Fuzzy Classification Algorithm for Imbalanced Data

2006 ◽  
Author(s):  
Le Xu ◽  
Mo-Yuen Chow ◽  
L.S. Taylor
Keyword(s):  
Data Mining ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Fuzzy Classification

MMD-encouraging convolutional autoencoder: a novel classification algorithm for imbalanced data

2021 ◽  
Author(s):  
Bin Li ◽  
Xiaofeng Gong ◽  
Chen Wang ◽  
Ruijuan Wu ◽  
Tong Bian ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Classification Algorithm ◽  
Convolutional Autoencoder

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

Data mining with a simulated annealing based fuzzy classification system

2008 ◽  
Vol 41 (5) ◽  
pp. 1824-1833 ◽  
Author(s):  
Hamid Mohamadi ◽  
Jafar Habibi ◽  
Mohammad Saniee Abadeh ◽  
Hamid Saadi
Keyword(s):  
Data Mining ◽  
Simulated Annealing ◽  
Classification System ◽  
Fuzzy Classification ◽  
Fuzzy Classification System

Imbalanced data classification algorithm with support vector machine kernel extensions

2018 ◽  
Vol 12 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Feng Wang ◽  
Shaojiang Liu ◽  
Weichuan Ni ◽  
Zhiming Xu ◽  
Zemin Qiu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Algorithm ◽  
Support Vector ◽  
Imbalanced Data Classification
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