Constraint nearest neighbor for instance reduction
- 84 Downloads
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.
KeywordsInstance reduction Natural neighbor Constraint nearest neighbor Instance-based learning
This work was supported by the National Natural Science Foundation of China (Nos. 61802360 and 61502060), the Project of Chongqing Education Commission (No. KJZH17104), the Fundamental Research Funds for the Central Universities (No. 2018NQN05) and the China Postdoctoral Science Foundation (No. 2016M602651).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants and animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- Bhattacharya B, Mukherjee K, Toussaint G (2005) Geometric decision rules for instance-based learning problems. In: International conference on pattern recognition and machine intelligence. Springer, pp 60–69Google Scholar
- Fayed HA, Atiya AF (2009) A novel template reduction approach for the \(k\)-nearest neighbor method. IEEE Trans Neural Netw 20(5):890–896Google Scholar
- Hamidzadeh J (2015) Irdds: Instance reduction based on distance-based decision surface. J AI Data Min 3(2):121–130Google Scholar
- Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 2016
- Marchiori E (2009) Graph-based discrete differential geometry for critical instance filtering. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 63–78Google Scholar
- Zhu Q, Feng J, Huang J (2016) Natural neighbor: a self-adaptive neighborhood method without parameter \(k\). Pattern Recognit Lett 80:30–36Google Scholar