Boosting KNNClassifier Performance with Opposition-Based Data Transformation
This work addresses classification performance enhancement for machine learning practitioners, but it is incremental as it applies an existing optimization technique to a new context.
The paper tackled improving KNN classifier performance by using Opposition-Based Learning (OBL) to generate synthetic opposite samples for data transformation, resulting in consistent outperformance over basic KNN on 26 heterogeneous and high-dimensional datasets.
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.