TY  - EJOU
AU  - Susmi, S. Jacophine 

TI  - Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 33
IS  - 2
SN  - 2326-005X

AB  - The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections (OLPP). Finally, gene classification is done by an OLSTM classifier. Here the traditional long short term memory (LSTM) is modified using parameter optimization which uses the adaptive artificial flora optimization (AAFO) algorithm. Based on the migration and flora reproduction process, the AAFO algorithm is stimulated. Using the accuracy, sensitivity, specificity, precision, recall, and f-measure, the proposed performance is analyzed. The test outcomes illustrate the effectiveness of the gene expression data classification with a 94.19% of accuracy value. The proposed gene expression data classification is implemented in the MATLAB platform.
KW  - Orthogonal locality preserving projections; recurrent neural network; artificial flora optimization

DO  - 10.32604/iasc.2022.023865