DocumentCode
2894921
Title
Lymphoma Cancer Classification Using NEWFM Based Filtering Method
Author
Hee-Jin Yoon ; Lim, J.S.
Author_Institution
IT Coll., Jangan Univ., Hwaseong, South Korea
fYear
2013
fDate
24-26 June 2013
Firstpage
1
Lastpage
2
Abstract
Since microarray data have gene data consisting of large amounts of data, to improve the performance of cancer classification, features of useful data should be extracted. The present paper presents a method of classifying lymphoma cancers by extracting 20 data each from 4026 lymphoma data out of microarray data through a t-test and Euclidean distances among filtering methods using the NEWFM (Neural Network with Weighted Fuzzy Membership Function) that uses the boundary sum of weight fuzzy membership functions.
Keywords
cancer; feature extraction; filtering theory; fuzzy neural nets; genetics; medical computing; pattern classification; statistical testing; Euclidean distances; NEWFM; boundary sum; data feature extraction; filtering method; gene data; lymphoma cancer classification; microarray data; neural network with weighted fuzzy membership function; supervised fuzzy neural network; t-test; Accuracy; Cancer; Data mining; Educational institutions; Feature extraction; Filtering; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location
Suwon
Print_ISBN
978-1-4799-0602-4
Type
conf
DOI
10.1109/ICISA.2013.6579505
Filename
6579505
Link To Document