Open AccessA Cluster Based Classification for Imbalanced Data Using SMOTE
Author(s) -
Ramesh Kumar Tripathi,
Linesh Raja,
Ankit Kumar,
Pankaj Dadheech,
Abhishek Kumar,
M N Nachappa
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012080
Subject(s) - oversampling , naive bayes classifier , data mining , computer science , random forest , classifier (uml) , process (computing) , artificial intelligence , pattern recognition (psychology) , machine learning , support vector machine , bandwidth (computing) , computer network , operating system
There is tremendous upturn in data repositories because of data generation by various organizations like government, cooperates, health caring in large amounts. Large amount of data is being produced, processed, collected, and analysed online. So there comes a requirement to transform this data into valuable information. This process of extracting the knowledge from large amount of data is referred as data mining. The proposed hybrid approach can be checked on different classifiers like Naïve Bayes, Random forest classifier etc. In proposed methodology we find that SMOTE algorithm which used K-nearest neighbour algorithm is limited to some minority class instances for creating synthetic samples, which sometimes leads to over fitting, so an effective oversampling approach can be developed.