Deep learning approach for microarray cancer data classification

Deep learning approach for microarray cancer data classification

Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data-structure, and higher variance of feature values. This has led to lesser classific...

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Bibliographic Details
Main Authors: Hema Shekar Basavegowda, Guesh Dagnew
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
pattern classification
entropy
biology computing
cancer
learning (artificial intelligence)
principal component analysis
neural net architecture
minimax techniques
feature extraction
lab-on-a-chip
deep learning approach
microarray cancer data classification
feature values
deep feedforward method
7-layer deep neural network architecture
min–max approach
binary cross-entropy
adaptive moment estimation
dimensionality reduction technique
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0028

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https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0028

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