Fractional-atom search algorithm-based deep recurrent neural network for cancer classification

Menaga, D.; Revathi, S.

doi:10.1007/s12652-021-03008-z

Cited by 5 publications

(4 citation statements)

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“…When comparing RN-Autoencoder to the results of FASO-DEEP-RNN introduced by Menaga et al. [ 30 ], RN-Autoencoder outperformed it by 7.13% in terms of the test accuracy with the colon dataset and 7.18% in terms of the test accuracy with the leukemia dataset. Figure 9 shows this comparison.…”

Section: Resultsmentioning

confidence: 99%

“…These studies are Pandit et.al [ 44 ], Devendra et al [ 28 ], Menaga et al. [ 30 ], Uzma et al [ 45 ], Majumder et al [ 33 ], Bustamam et al [ 35 ], Samieinasab et al [ 47 ], Singh et al [ 48 ] and Bacha et al [ 50 ]. Also, with each comparison we use only the metrics and datasets used by the comparative study.…”

Section: Resultsmentioning

confidence: 99%

“…Menaga et al. [ 30 ] used wrapper method for feature selection in their introduced model for classifying cancer from colon and leukemia datasets. the selected features have been classified using Deep Recurrent Neural Network (Deep RNN), which is optimized by the utilization of Fractional-Atom Search Algorithm (FASO).…”

Section: Related Workmentioning

confidence: 99%

“…[ 26 ] 2022 4 - SMOTE Resampling + L2-SVM Kakati et al [ 27 ] 2022 17 - Transfer learning + CNN Dai et al [ 28 ] 2021 3 - ERGCN Single Stage Feature Selection Mohammed et al [ 29 ] 2021 5 Lasso Staking Ensemble of CNN Menaga et al. [ 30 ] 2021 2 Wrapper Fractional-ASO Deep RNN Al Mamun et al [ 31 ] 2021 12 mrCAE - Multiple Stages Feature Selection Majumder et al [ 33 ] 2022 4 ANOVA, IG MLP, 1DCNN, 2DCNN Saberi-Movahed et al [ 34 ] 2022 9 DR-FS-MFMR = Matrix Factorization + Minimum Redundancy Unsupervised clustering Bustamam et al [ 35 ] 2021 2 SVM-RFE + ABC SVM Samieinasab et al [ 47 ] 2022 1 Ensemble (Variance Inflation Factor, Pearson’s Correlation, Information G...…”

Section: Related Workmentioning

confidence: 99%

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RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

et al. 2023

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Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets. These characteristics have a negative impact on the performance of most classifiers when used to classify cancer using genomic datasets. Results This paper introduces Reduced Noise-Autoencoder (RN-Autoencoder) for pre-processing imbalanced genomic datasets for precise cancer classification. Firstly, RN-Autoencoder solves the curse of dimensionality problem by utilizing the autoencoder for feature reduction and hence generating new extracted data with lower dimensionality. In the next stage, RN-Autoencoder introduces the extracted data to the well-known Reduced Noise-Synthesis Minority Over Sampling Technique (RN- SMOTE) that efficiently solve the problem of class imbalance in the extracted data. RN-Autoencoder has been evaluated using different classifiers and various imbalanced datasets with different imbalance ratios. The results proved that the performance of the classifiers has been improved with RN-Autoencoder and outperformed the performance with original data and extracted data with percentages based on the classifier, dataset and evaluation metric. Also, the performance of RN-Autoencoder has been compared to the performance of the current state of the art and resulted in an increase up to 18.017, 19.183, 18.58 and 8.87% in terms of test accuracy using colon, leukemia, Diffuse Large B-Cell Lymphoma (DLBCL) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. Conclusion RN-Autoencoder is a model for cancer classification using imbalanced gene expression datasets. It utilizes the autoencoder to reduce the high dimensionality of the gene expression datasets and then handles the class imbalance using RN-SMOTE. RN-Autoencoder has been evaluated using many different classifiers and many different imbalanced datasets. The performance of many classifiers has improved and some have succeeded in classifying cancer with 100% performance in terms of all used metrics. In addition, RN-Autoencoder outperformed many recent works using the same datasets.

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Section: Resultsmentioning

confidence: 99%