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
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;
Conference_Titel :
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location :
Suwon
Print_ISBN :
978-1-4799-0602-4
DOI :
10.1109/ICISA.2013.6579505
