Constraint nearest neighbor for instance reduction
| العنوان: | Constraint nearest neighbor for instance reduction |
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| المؤلفون: | Lijun Yang, Xiaolu Hong, Dongdong Cheng, Qingsheng Zhu, Quanwang Wu, Jinlong Huang |
| المصدر: | Soft Computing. 23:13235-13245 |
| بيانات النشر: | Springer Science and Business Media LLC, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Dependency (UML), Computer science, Computational intelligence, 02 engineering and technology, Theoretical Computer Science, k-nearest neighbors algorithm, Constraint (information theory), Set (abstract data type), Reduction (complexity), Core (game theory), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, Decision boundary, 020201 artificial intelligence & image processing, Geometry and Topology, Algorithm, Software |
| الوصف: | In instance-based machine learning, algorithms often suffer from prohibitive computational costs and storage space. To overcome such problems, various instance reduction techniques have been developed to remove noises and/or redundant instances. Condensation approach is the most frequently used method, and it aims to remove the instances far away from the decision surface. Edition method is another popular one, and it removes noises to improve the classification accuracy. Drawbacks of these existing techniques include parameter dependency and relatively low accuracy and reduction rate. To solve these drawbacks, the constraint nearest neighbor-based instance reduction (CNNIR) algorithm is proposed in this paper. We firstly introduce the concept of natural neighbor and apply it into instance reduction to eliminate noises and search core instances. Then, we define a constraint nearest neighbor chain which only consists of three instances. It is used to select border instances which can construct a rough decision boundary. After that, a specific strategy is given to reduce the border set. Finally, reduced set is obtained by merging border and core instances. Experimental results show that compared with existing algorithms, the proposed algorithm effectively reduces the number of instances and achieves higher classification accuracy. Moreover, it does not require any user-defined parameters. |
| تدمد: | 1433-7479 1432-7643 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::332ac76a0a08c646e6f552906086380d https://doi.org/10.1007/s00500-019-03865-z |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........332ac76a0a08c646e6f552906086380d |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14337479 14327643 |
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