Affiliations: [a] Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China
| [b] Department of Computer, China University of Petroleum, Beijing, China
Correspondence:
[*]
Corresponding author. Li Hongqi, Key Lab of Petroleum Data Mining of Beijing, China University of Petroleum, Beijing 102249, China. Tel.: +81 10 80116740; E-mail: [email protected].
Abstract: The k-nearest-neighbor classifier is a vital algorithm. In practice, the choice of k is decided by the cross-validation method. We propose a new method for neighborhood size selection based on the data set profile. The distribution of a data set and its intrinsic characteristics are the fundamental factors to the choice of k. A local complexity was computed for each example and a complexity profile was constructed by sorting these local complexity values which try to capture inner structure of a data set. After this, a feature vector was built by combing the local complexity profile and some statistic features of a data set. In addition, a history meta-data set was constructed by using the feature vector as attributes and the optimum k value of data set as the label, which was calculated by using ten cross-validation methods. A predict model was trained based on the historic meta-data set and used to predict optimum k value for a new data set. Some exclusive experiments are conducted to verify the proposed method. The results shows that the local complexity features could reflect the inner structure of a data set which could help find the optimum k for k-NN for different domains.
Keywords: k-NN classifier, data sets, local complexity profile, optimum k
