Automatic Classifier Selection Based on Classification Complexity
Choosing a proper classifier for one specific data set is important in practical application.Automatic classifier selection(CS)aims to recommend the most suitable classifiers to a new data set based on the similarity with the historical data sets.The key step of CS is the extraction of data set feature.This paper proposes a novel data set feature that characterizes the classification complexity of problems,which has a close connection with the performance of classifiers.We highlight two contributions of our work: firstly,our feature can be computed in a low time complexity; secondly,we theoretically show that our feature has connection with generalization errors of some classifiers.Empirical results indicate that our feature is more effective and efficient than the existing data set features.
Automatic classifier selection Data set feature Data set similarity
Liping Deng Wen-Sheng Chen Binbin Pan
College of Mathematics and Statistics,Shenzhen University,Shenzhen 518060,Peoples Republic of China College of Mathematics and Statistics,Shenzhen University,Shenzhen 518060,Peoples Republic of China
国际会议
广州
英文
292-303
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）