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Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information

التفاصيل البيبلوغرافية
العنوان: Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information
المؤلفون: K. Dhana Sree Devi, P. Karthikeyan, Usha Moorthy, K. Deeba, V. Maheshwari, Shaikh Muhammad Allayear
المصدر: Mathematical Problems in Engineering. 2022:1-10
بيانات النشر: Hindawi Limited, 2022.
سنة النشر: 2022
مصطلحات موضوعية: ComputingMethodologies_PATTERNRECOGNITION, Article Subject, General Mathematics, General Engineering
الوصف: Microarray data are becoming a more essential source of gene expression data for interpretation and analysis. To improve the detection accuracy of tumors, the researchers try to use the lowest feasible collection of the most gene expression studies, and relevant gene expression patterns are found. The purpose of this article is to use a data mining strategy and an optimized feature selection method focused on a limited dense tree forest classifier to evaluate and forecast colon cancer data. More specifically, merging the “gain information” and “Grey wolf optimization” was incorporated as a feature selection approach into the random forest classifier, to improve the prediction model’s accuracy. Our suggested technique can decrease the load of high-dimensional data, and it allows quicker computations. In this research, we provided a comparison of the analysis model with feature selection accuracy over model analysis without feature selection accuracy. The extensive experimental findings have shown that the suggested method with selecting features is beneficial, outperforming the good classification performances.
وصف الملف: text/xhtml
تدمد: 1563-5147
1024-123X
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43c6e87c000a9179247b9f764010977c
https://doi.org/10.1155/2022/4092404
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....43c6e87c000a9179247b9f764010977c
قاعدة البيانات: OpenAIRE
الوصف
تدمد:15635147
1024123X