DocumentCode :
1785044
Title :
ELMDF: A new classification algorithm based on Data Field
Author :
Shuliang Wang ; Dakui Wang
Author_Institution :
Sch. of Software, Beijing Inst. of Technol., Beijing, China
fYear :
2014
fDate :
2-5 Nov. 2014
Firstpage :
28
Lastpage :
33
Abstract :
In this paper, a novel classification algorithm, ELMDF (Extreme Learning Machine based on Data Field), is proposed to solve the problem of estimating the number of hidden layer neurons in typical ELM. For constructing ELMDF, a new theory based on data field, FMDF (Fundamental Matrix of Data Field) is proposed in this paper. The breast cancer cell image dataset, and the genome dataset are used to test and illustrate the proposed method. The experimental case demonstrates that ELMDF performs better than other six typical supervised learning algorithms on different datasets.
Keywords :
bioinformatics; cancer; cellular biophysics; genomics; image classification; learning (artificial intelligence); medical image processing; ELMDF; FMDF; breast cancer cell image dataset; classification algorithm based on data field; extreme learning machine based on data field; fundamental matrix of data field; genome dataset; hidden layer neurons; supervised learning algorithms; Accuracy; Bioinformatics; Breast cancer; Classification algorithms; Genomics; Neurons; Potential energy; Data Field; ELM; ELMDF; FMDF; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on
Conference_Location :
Belfast
Type :
conf
DOI :
10.1109/BIBM.2014.6999278
Filename :
6999278
Link To Document :
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