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- W4289236186 abstract "Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems. The objectives of feature selection include: building simpler and more comprehensible models, improving data mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity based, information theoretical based, sparse learning based and statistical based methods. To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (url{http://featureselection.asu.edu/}). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research." @default.
- W4289236186 created "2022-08-01" @default.
- W4289236186 creator A5002709735 @default.
- W4289236186 creator A5011048500 @default.
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- W4289236186 creator A5040639891 @default.
- W4289236186 creator A5053337874 @default.
- W4289236186 creator A5061243456 @default.
- W4289236186 date "2017-12-06" @default.
- W4289236186 modified "2023-10-17" @default.
- W4289236186 title "Feature Selection" @default.
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