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Lung Cancer Subtyping from Gene Expression Data using General and Enhanced Fuzzy Min-Max Neural Networks
  • Yashpal Singh,
  • Seba Susan
Yashpal Singh
Delhi Technological University
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Seba Susan
Delhi Technological University

Corresponding Author:[email protected]

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Cancer diagnosis using gene expression data is significant research for facilitating early treatment and prevention of cancer. The classification of gene expression data is challenging due to its high dimensionality and smaller number of samples that renders classification a difficult task. Creation of well-defined class boundaries is the aim of every classification algorithm. The Fuzzy min-max (FMM) neural network classifier is known to create good decision boundaries using hyperboxes constructed for each class. In this paper, we explore the General Fuzzy min-max (GFMM) and Enhanced Fuzzy min-max (EFMM) neural network architectures for the classification of lung cancer subtypes from microarray gene expression data. Both GFMM and EFMM are advanced versions of Simpson’s FMM neural network classifier. The GFMM is extremely efficient because it involves very simple operations for hyperbox manipulation, and can handle both labeled and unlabeled data. On the other hand, EFMM proposes three heuristic rules related to hyperbox expansion, contraction and the overlap test, which enhances the learning algorithm. We perform the classification of gene expression data using these two algorithms, then we analyze the performance by visualizing the hyperboxes obtained after training, and compare the accuracies of these classifiers. LASSO is used for selecting the important genes from the high-dimensional gene expression data. After the analysis of the results, we observe that EFMM with LASSO gives the best performance as compared to GFMM, FMM and other machine learning algorithms.
05 Dec 2022Submitted to Engineering Reports
07 Dec 2022Submission Checks Completed
07 Dec 2022Assigned to Editor
10 Dec 2022Review(s) Completed, Editorial Evaluation Pending
13 Dec 2022Reviewer(s) Assigned
08 Feb 2023Editorial Decision: Revise Major
21 Mar 20231st Revision Received
22 Mar 2023Submission Checks Completed
22 Mar 2023Assigned to Editor
22 Mar 2023Review(s) Completed, Editorial Evaluation Pending
27 Mar 2023Reviewer(s) Assigned
03 Apr 2023Editorial Decision: Accept