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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
564

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FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction

Angelos Chatzimparmpas, Rafael M Martins, Kostiantyn Kucher

    IEEE Transactions on Visualization and Computer Graphics
    |January 6, 2022
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    Summary
    This summary is machine-generated.

    FeatureEnVi enhances machine learning (ML) by providing a visual analytics system for feature engineering. It aids in selecting, transforming, and combining features to improve model performance and transparency.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Science

    Background:

    • The machine learning (ML) life cycle includes data gathering, preparation, and algorithm selection.
    • Feature engineering significantly improves ML model performance, computational efficiency, and transparency.
    • Existing visual analytics tools inadequately support the feature engineering stage of the ML life cycle.

    Purpose of the Study:

    • To introduce FeatureEnVi, a novel visual analytics system designed to assist users with the feature engineering process.
    • To provide tools for selecting important features, transforming existing ones, and generating new feature combinations.
    • To enable exploration of feature impact on local and global scales through data space slicing.

    Main Methods:

    • FeatureEnVi integrates automatic feature selection techniques.
    • The system visually guides users with statistical evidence on feature influence.
    • It supports the extraction of engineered features evaluated by multiple validation metrics.

    Main Results:

    • FeatureEnVi facilitates the selection, transformation, and combination of features for enhanced ML models.
    • Data space slicing allows for detailed analysis of feature impact.
    • The system was demonstrated through two use cases and a case study, with positive feedback from ML experts.

    Conclusions:

    • FeatureEnVi effectively addresses the inadequacy of current tools in supporting feature engineering.
    • The system empowers users to create powerful, engineered features, leading to improved ML outcomes.
    • FeatureEnVi offers a valuable solution for optimizing the ML workflow through advanced feature engineering support.