Stability Investigation of Ensemble Feature Selection for High Dimensional Data Analytics

Sumant, Archana Shivdas; Patil, Dipak V.

doi:10.1007/978-3-031-12413-6_63

Cited by 1 publication

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“…For high dimensional data, the two techniques mentioned in this paper perform well. In [34], the stability analysis of the same methodology is covered. When choosing features, stability is a significant factor.…”

Section: 𝐻 𝑥 𝑝 𝑋 Log 𝑝 𝑋 𝑑𝑥 (4)mentioning

confidence: 99%

Parallel Ensemble Feature Subset Selection based Deep Learning Approach for Imbalanced High Dimensional Cancer Datasets

Sumant¹,

Patil²

2022

INDJCSE

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Cancer is currently one of the most threatening diseases. The analysis of high dimensional microarray data is a method that is frequently used for cancer diagnosis. Multi category imbalance data analysis in the medical sciences plays crucial role. The growing number of cancer patients has made the necessity for cancer classification absolutely necessary. In this study, PE-SU-R-CNN and PE-ChS-R-CNN are employed as parallel ensemble feature selection based deep learning classifiers to address issues for multi-class cancer classification. The deep learning convolutional neural network with soft-max activation function at decision layer is used. The most well known nine cancer microarray gene expression datasets are used in the experiments. The proposed system's empirical findings are compared with deep learning techniques based on bio-inspired feature selection firefly and elephant searches. Finally, a one-way ANOVA statistical significance test with a post hoc Tukey's test is used to draw several inferences about the optimal classifier model to use. The proposed parallel model shows significant improvement over existing methods.

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Section: 𝐻 𝑥 𝑝 𝑋 Log 𝑝 𝑋 𝑑𝑥 (4)mentioning

confidence: 99%