用反向学习生成新数据，让KNN分类更准
Boosting KNNClassifier Performance with Opposition-Based Data Transformation
- 用反向学习生成对立样本，丰富训练数据
- 在26个数据集上均优于普通KNN，提升明显
- 适合处理复杂稀疏数据，轻量高效
本文提出一种基于反向学习（OBL）的新数据变换框架，用于提升传统分类算法性能。原本用于加速优化收敛的OBL被用来生成合成的对立样本，以丰富训练数据并改善决策边界。我们探索了三种OBL变体：全局OBL、类别级OBL和局部类别级OBL，并将其与K近邻（KNN）结合。在26个异构且高维的数据集上进行的大量实验表明，采用OBL增强的分类器始终优于基础KNN。结果表明，OBL是一种轻量但强大的数据变换策略，特别适用于复杂或稀疏的学习环境。
原文摘要 · Abstract (English)
In this paper, we introduce a novel data transformation framework based on Opposition-Based Learning (OBL) to boost the performance of traditional classification algorithms. Originally developed to accelerate convergence in optimization tasks, OBL is leveraged here to generate synthetic opposite samples that enrich the training data and improve decision boundary formation. We explore three OBL variants Global OBL, Class-Wise OBL, and Localized Class-Wise OBL and integrate them with K-Nearest Neighbors (KNN). Extensive experiments conducted on 26 heterogeneous and high-dimensional datasets demonstrate that OBL-enhanced classifiers consistently outperform the basic KNN. These findings underscore the potential of OBL as a lightweight yet powerful data transformation strategy for enhancing classification performance, especially in complex or sparse learning environments.
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