Fast cancer diagnosis based on extreme learning machine

doi:10.3969/j.issn.0253-2778.2018.02.010

Abstract

Abstract: The local receptive fields based extreme learning machine (ELM-LRF) method was utilized to learn the effective features from the acquired gene expression data to help enhance cancer diagnosis and classification. Firstly, the principal component analysis (PCA) method was implemented to process the dataset. Secondly, the features mapping to map our dataset were constructed to the specific feature space. Finally, the features to train the learning model were used to get the final ELM feature extraction model. The experiment shows that the proposed algorithm outperforms almost all the existing methods in accuracy and efficiency.

Key words: extreme learning machine (ELM), feature learning, machine learning, classification, cancer diagnosis

CLC Number:

TP391

LIN Yupeng, XIE Zhige, XU Kai, CHEN Fei, LIU Ligang. Fast cancer diagnosis based on extreme learning machine[J]. Journal of University of Science and Technology of China, 2018, 48(2): 154-160.

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