文章基本信息

标题：Efficacy of Non-negative Matrix Factorization for Feature Selection in Cancer Data
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作者：Parth Patel ; Kalpdrum Passi ; Chakresh Kumar Jain 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2020
卷号：10
期号：4
页码：1-20
DOI：10.5121/ijdkp.2020.10401
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
关键词：Microarray datasets;Feature Extraction;Feature Selection;Principal Component Analysis;Non-negative Matrix Factorization;Machine learning.