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Feature selection using a neural framework with controlled redundancy.

Rudrasis Chakraborty, Nikhil R Pal

    IEEE Transactions on Neural Networks and Learning Systems
    |December 23, 2014
    PubMed
    Summary
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    This study introduces novel feature selection methods, FSMLP-CoR and mFSMLP-CoR, for machine learning. These techniques effectively select essential features while managing redundancy for improved classification and prediction tasks.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Feature selection is crucial for effective model performance.
    • Existing methods may select redundant features or fail to capture complex interactions.
    • Multilayer perceptron (MLP) neural networks offer a powerful framework for feature analysis.

    Purpose of the Study:

    • To develop advanced feature selection methods for classification and function approximation.
    • To introduce controlled redundancy (CoR) into feature selection to manage feature dependencies.
    • To enhance model performance and robustness by optimizing feature subsets.

    Main Methods:

    • A feature selection method based on multilayer perceptron (MLP) neural network, termed FSMLP, was developed.

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  • A general scheme, FSMLP-CoR, was proposed to select features with controlled redundancy.
  • A new training scheme, mFSMLP-CoR, was introduced to improve system performance and reduce weight initialization dependency.

    • Main Results:

    • FSMLP effectively selects essential features and discards irrelevant ones.
    • FSMLP-CoR and mFSMLP-CoR demonstrated effectiveness in selecting features with controlled redundancy across various datasets.
    • The selected features were shown to be adequate for solving the problems, accounting for nonlinear interactions.

    Conclusions:

    • The proposed FSMLP-CoR and mFSMLP-CoR schemes offer significant advantages in feature selection, including integrated system design and handling of nonlinear interactions.
    • mFSMLP-CoR not only enhances system performance but also reduces sensitivity to initial network weights.
    • These methods provide a robust approach to feature selection, applicable to diverse machine learning problems.