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- W2899346980 abstract "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." @default.
- W2899346980 created "2018-11-09" @default.
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- W2899346980 date "2018-01-01" @default.
- W2899346980 modified "2023-09-28" @default.
- W2899346980 title "Automatic Classifier Selection Based on Classification Complexity" @default.
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- W2899346980 doi "https://doi.org/10.1007/978-3-030-03338-5_25" @default.
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