Neural Network Feature Selection (NNFS) for Incomplete and High-Dimensional Data

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Bagherpour Negin; Ebrahimi Behrang

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Journal Paper

Paper Information

Journal: JOURNAL OF ALGORITHMS AND COMPUTATION Year:2025 | Volume:57 | Issue:1 Page(s): 80-95

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Information Journal Paper

Title

Neural Network Feature Selection (NNFS) for Incomplete and High-Dimensional Data

Author(s)

Bagherpour Negin | Ebrahimi Behrang | Issue Writer Certificate

Keywords

Feature Selection

high dimension

medium dimension

Incomplete data

neural network

Abstract

Feature Selection is a critical step in machine learning, especially when dealing with high-dimensional and Incomplete data. Traditional methods often struggle with missing values, which are common in real-world applications. This paper introduces neural network Feature Selection (NNFS), a novel deep learning-based approach that effectively identifies important features even in the presence of missing data. We provide a variety of comparisons to evaluate the suggested algorithm over existing methods. We demonstrate the accuracy, speed and sensitivity to missed data. According to numerical results, the proposed algorithm outperforms existing methods especially for medium size datasets. Both random and real tests are presented to make the results more realistic.

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