关键词: 万有引力, 近邻策略, 多视角学习, 不平衡数据, 机器学习

Abstract: When the traditional neighbor classifier deals with the imbalanced problem, its criterion function tends to bias the test sample to majority class and the parameters are strongly dependent on the data set. To overcome the drawbacks in NN-based classifiers, the Gravitational Fixed Radius Nearest Neighbor classifier（GFRNN） based on the law of universal gravitation realizes a parameterless and efficient classifier for imbalanced data. However, it is unreasonable that GFRNN adopting only Euclidean distance to calculate radius and select candidates. To this end, this paper proposes a Multi-view learning with Gravitational Fixed Radius Nearest Neighbor algorithm（MGFRNN）. In MGFRNN, L1-norm, Euclidean and Chebyshev distance are adopted to realize multi-view learning. The experimental result validates that the proposed MGFRNN achieves the highest classification accuracy among comparison algorithms.

Key words: gravitation, nearest neighbor, multi-view learning, imbalanced data, machine learning