大海捞针:对分类任务特征选择的见解

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2023-11-03 DOI:10.1007/s10844-023-00823-y
Laura Morán-Fernández, Verónica Bolón-Canedo
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引用次数: 0

摘要

大数据的增长导致了可用数据量的压倒性增长,包括特征的数量。特征选择,即选择相关特征并丢弃不相关特征的过程,已被成功地用于降低数据集的维数。然而,由于文献中有许多特征选择方法,确定针对特定问题的最佳策略并不简单。在本研究中,我们将各种特征选择方法的性能与随机选择方法进行比较,以确定针对给定类型问题的最有效策略。我们使用大量的数据集来涵盖广泛的现实世界挑战。我们评估了七种流行的特征选择方法和五种分类器的性能。我们的研究结果表明,特征选择是机器学习中有价值的工具,基于相关性的特征选择是最有效的策略,无论场景如何。此外,我们发现使用不适当的阈值与排名方法产生的结果与随机选择特征子集一样差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Finding a needle in a haystack: insights on feature selection for classification tasks
Abstract The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding irrelevant ones, has been successfully used to reduce the dimensionality of datasets. However, with numerous feature selection approaches in the literature, determining the best strategy for a specific problem is not straightforward. In this study, we compare the performance of various feature selection approaches to a random selection to identify the most effective strategy for a given type of problem. We use a large number of datasets to cover a broad range of real-world challenges. We evaluate the performance of seven popular feature selection approaches and five classifiers. Our findings show that feature selection is a valuable tool in machine learning and that correlation-based feature selection is the most effective strategy regardless of the scenario. Additionally, we found that using improper thresholds with ranker approaches produces results as poor as randomly selecting a subset of features.
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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