Impact of imbalanced features on large datasets.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2025-03-13 eCollection Date: 2025-01-01 DOI:10.3389/fdata.2025.1455442
Waleed Albattah, Rehan Ullah Khan
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引用次数: 0

Abstract

The exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images. However, feature-based classification faces a challenge due to uneven class instance distribution. Ideally, each class should have an equal number of instances and features to ensure optimal classifier performance. However, real-world scenarios often exhibit class imbalances. Thus, this article explores the classification framework based on image features, analyzing balanced and imbalanced distributions. Through extensive experimentation, we examine the impact of class imbalance on image classification performance, primarily on large datasets. The comprehensive evaluation shows that all models perform better with balancing compared to using an imbalanced dataset, underscoring the importance of dataset balancing for model accuracy. Distributed Gaussian (D-GA) and Distributed Poisson (D-PO) are found to be the most effective techniques, especially in improving Random Forest (RF) and SVM models. The deep learning experiments also show an improvement as such.

不平衡特征对大型数据集的影响。
图像和视频数据的指数级增长激发了对实用的实时基于内容的搜索算法的需求。特征在识别图像中的物体方面起着至关重要的作用。然而,由于类实例分布不均匀,基于特征的分类面临挑战。理想情况下,每个类应该具有相同数量的实例和特征,以确保最佳的分类器性能。然而,现实世界的场景经常表现出类的不平衡。因此,本文探索了基于图像特征的分类框架,分析了平衡分布和不平衡分布。通过大量的实验,我们研究了类不平衡对图像分类性能的影响,主要是在大型数据集上。综合评价表明,与使用不平衡数据集相比,所有模型在平衡时都表现得更好,强调了数据集平衡对模型精度的重要性。分布高斯(D-GA)和分布泊松(D-PO)被认为是最有效的技术,特别是在改进随机森林(RF)和支持向量机模型方面。深度学习实验也显示出这样的改进。
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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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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