微阵列数据集的特征选择技术:综述

Avinash Nagaraja, S. Sinha, Shivamurthaiah Mallaiah
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引用次数: 0

摘要

对于许多研究特征选择技术的人员来说,从微阵列数据集中找到合适的特征已成为一个瓶颈。研究人员经常创建特征选择方法和算法,目的是提高微阵列数据集的准确性。本研究的主要目的是介绍各种当代特征选择技术,如针对微阵列数据集提出的过滤器、封装器和嵌入式方法,以解决多类分类问题和不同方法,从而提高学习算法的性能,解决数据集中的不平衡问题,并为微阵列数据集的研究工作提供支持。本研究基于 "关键评论问题"(Critical Review Questions,CRQ),采用了评论方法学中描述的特征选择方法,并应用于微阵列数据集。我们讨论了使用微阵列数据集解决多类分类问题的特征选择策略的分析结果和未来前景,以及加速计算环境的前瞻性方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature selection techniques for microarray dataset: a review
For many researchers working on feature selection techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create feature selection approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary feature selection techniques, such as Filter, Wrapper, and Embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on Critical Review Questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of feature selection strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment
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