期刊
KNOWLEDGE-BASED SYSTEMS
卷 243, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2022.108451
关键词
KNN rule; Classification; Prototype selection; Local feature weighting
资金
- National Key R&D Program of China [2018YFB1403600]
- Grad-uate Research and Innovation Foundation of Chongqing, China [CYB20050]
- National Natural Science Foundation of China [71801164, 71471022]
- Funda-mental Research Funds for the Central Universities [2021CDJSKJC10]
K-Nearest Neighbors (KNN) rule is a powerful classification technique but has drawbacks. In this study, an improved KNN rule, IKNN_PSLFW, combining prototype selection and local feature weighting, is proposed to address these issues and achieve promising classification performance.
K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in machine learning. Nevertheless, it suffers from some drawbacks such as high memory consumption, low time efficiency, class overlapping and difficulty of setting an appropriate K value. In this study, we propose an Improved K-Nearest Neighbor rule combining Prototype Selection and Local Feature Weighting (IKNN_PSLFW) to address the above issues in one framework. Differing from conventional prototype selection, IKNN_PSLFW not only selects the representative instances as prototypes, but also preserves the information of instances that are not selected. To deal with the class overlapping problem, IKNN_PSLFW explores the feature relevance in local regions by assigning different weights to different features. For an instance with unknown class label, IKNN_PSLFW uses three classification rules corresponding to three scenarios, according to the distance between the instance and each prototype, for classification. To evaluate the performance of IKNN_PSLFW, we conduct experimental study on 20 benchmark datasets. The experimental results show that compared with the conventional KNN rule, some state-of-the-art prototype selection methods and other machine learning algorithms, the proposed IKNN_PSLFW achieves promising classification performance with high time efficiency.(c) 2022 Elsevier B.V. All rights reserved.
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