Ensemble based Fuzzy-Rough Nearest Neighbor Approach for Classification of Cancer from Microarray data
Year: 2019
Type: article
Abstract: Cancer sample classification from gene expression data is one of the challenging areas of research in biomedical engineering and machine learning.In gene expression data, labeled samples are very limited in comparison to unlabeled samples; and labeling of unlabeled data is costly.Therefore, single classifier trained with limited training samples often fails to produce required accuracy.In such situation, ensemble technique can be effective as it combines the results of individual classifier which can improve the cancer classification accuracy.In this article a novel ensemble based fuzzy-rough nearest neighbour (EnFRNN) for cancer sample classification from microarray gene expression data is proposed.The proposed method is able to deal the uncertainty, overlapping and indiscernibility generally present in cancer subtype classes of the gene expression data.The proposed ensemble classifier is tested on eight publicly available microarray gene expression datasets.Experimental results suggest that the performance of the proposed ensemble classifier provides better results in comparison to individual classifier for cancer classification from gene expression data.In summary, fuzzy-rough based ensemble learning method turns out to be very effective in cancer sample classification from gene expression data particularly when the individual classifier result is not up to the mark with limited training samples.
Authors: Ansuman Kumar, Anindya Halder
Institution: North Eastern Hill University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W2971576832