Application of Weight-Based Feature Selection in Artificial Neural Network with Transition Layer
Author: 
Yuan FengAuthors Info & Claims
Pages 51 - 55
Abstract
In the traditional artificial intelligence network model, the interpret ability of the model is often not strong due to the intricate associations between neurons, and the importance of input layer neurons in the model is difficult to be judged intuitively. This paper attempts to propose a new type of artificial neural network structure, which adds a transition layer between the input layer and the first hidden layer to enhance the interpret ability of the model for further feature selection. Simulated data set is applied to test the accuracy of feature selection and prediction, and a variant group lasso is added to improve the effect of feature selection. Result shows that the performance of new-structured artificial neural network improves in feature selection with the variant group lasso added.
References
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Kan Chen, Zhiqi Bu, Shiyun Xu, Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm, arXiv preprint arXiv:2107.01266, 2021 - arxiv.org
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Li Y., Chen CY., Wasserman W.W. (2015) Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters. In: Przytycka T. (eds) Research in Computational Molecular Biology. RECOMB 2015. Lecture Notes in Computer Science, vol 9029. Springer, Cham. https://doi.org/10.1007/978-3-319-16706-0_20
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Published In
March 2022
291 pages
ISBN:9781450395748
DOI:10.1145/3529399
Copyright © 2022 ACM.
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Association for Computing Machinery
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Published: 10 June 2022
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ICMLT 2022
ICMLT 2022: 2022 7th International Conference on Machine Learning Technologies
March 11 - 13, 2022
Rome, Italy
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