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引用次数: 0
摘要
不平衡数据集是机器学习和人工智能领域的一个难题。由于大多数模型通常假定数据分布平衡,因此不平衡的正负实例会导致预测或分类任务出现严重偏差。目前的过采样方法经常会遇到过拟合和边界偏差等问题。本文提出了一种称为 SVM-GA 过度采样(SGO)的新型不平衡数据增强技术,它将支持向量机(SVM)与遗传算法(GA)相结合。我们的方法利用 SVM 来识别决策边界,并使用 GA 沿此边界生成新的少数样本,从而有效地解决了过拟合和边界偏差问题。实验验证了 SGO 在大多数数据集上的表现优于传统方法,为解决不平衡数据问题提供了一种新颖有效的方法,具有潜在的应用前景和推广价值。
SGO: An innovative oversampling approach for imbalanced datasets using SVM and genetic algorithms
Imbalanced datasets present a challenging problem in machine learning and artificial intelligence. Since most models typically assume balanced data distributions, imbalanced positive and negative examples can lead to significant bias in prediction or classification tasks. Current over-sampling methods frequently encounter issues like overfitting and boundary bias. A novel imbalanced data augmentation technique called SVM-GA over-sampling (SGO) is proposed in this paper, which integrates Support Vector Machines (SVM) with Genetic Algorithms (GA). Our approach leverages SVM to identify the decision boundary and uses GA to generate new minority samples along this boundary, effectively addressing both over-fitting and boundary biases. It has been experimentally validated that SGO outperforms the traditional methods on most datasets, providing a novel and effective approach to address imbalanced data problems, with potential application prospects and generalization value.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.