An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory

Author(s):  
Na Jiao ◽  
Duoqian Miao
2013 ◽  
Vol 2 (4) ◽  
pp. 33-46 ◽  
Author(s):  
P. K. Nizar Banu ◽  
H. Hannah Inbarani

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.


Complexity ◽  
2014 ◽  
Vol 20 (5) ◽  
pp. 50-62 ◽  
Author(s):  
Mohammad Taghi Rezvan ◽  
Ali Zeinal Hamadani ◽  
Seyed Reza Hejazi

2013 ◽  
Vol 8 (6) ◽  
Author(s):  
Jia Wu ◽  
Zhihua Cai ◽  
Xiaolin Chen ◽  
Meng Li ◽  
Bin Guo

Sign in / Sign up

Export Citation Format

Share Document