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Construction of cascaded depth model based on boosting feature selection and classification

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Abstract

Artificial intelligence is an important research direction in the field of computer science. Its vision is to better understand the world around us. In this paper, the specific feature transformation, feature selection and classifier algorithm used in the framework are studied and analyzed, and a cascade depth model is constructed. Through detailed analysis of the feature transformation, feature selection and classification methods used in the framework, an effective cascade depth model based on feature extraction and feature selection is successfully implemented, and the effectiveness of the proposed feature combination method is verified.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shanxi Agricultural University, Taigu, 030801, Shanxi, China

    Hongwen Yan, Zhenyu Liu & Qingliang Cui

Authors
  1. Hongwen Yan
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  2. Zhenyu Liu
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  3. Qingliang Cui
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Correspondence to Qingliang Cui.

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Yan, H., Liu, Z. & Cui, Q. Construction of cascaded depth model based on boosting feature selection and classification. Evol. Intel. 15, 2395–2402 (2022). https://doi.org/10.1007/s12065-020-00413-9

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  • DOI: https://doi.org/10.1007/s12065-020-00413-9

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