The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification

Abstract

Microarray data classification has a great challenge due to number of samples which is much smaller compared to the number of genes. The problem is getting harder when the dataset has multiclass target and the number of samples in each class is not well distributed (which is called imbalance data distribution). In this research, two different approaches to handle imbalance data distribution are studied, they are SMOTE (based on data approach) and weighted ELM (based on algorithmic approach). To evaluate the performance of the proposed method, two public imbalanced multiclass microarray dataset are used, GCM (Global Cancer Map) and Subtypes-Leukemia dataset. The results of experiment show that the implementation of SMOTE and weighted ELM on GCM dataset have no significant effect in the classification performance. Different with the Subtypes-Leukemia dataset, the implementation of SMOTE and weighted ELM has improved the classification performance compared to the previous research. Generally, the results show that weighted ELM perform slightly better compared to SMOTE to increase the accuracy of the minority class.