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univariate feature ranking for classification using chi-square tests  (MathWorks Inc)


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    MathWorks Inc univariate feature ranking for classification using chi-square tests
    ( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
    Univariate Feature Ranking For Classification Using Chi Square Tests, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/chi-squared+test+using/pmc11794476-218-1-12
    Average 90 stars, based on 1 article reviews
    univariate feature ranking for classification using chi-square tests - by Bioz Stars, 2026-10
    90/100 stars

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    1) Product Images from "Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact"

    Article Title: Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact

    Journal: Scientific Reports

    doi: 10.1038/s41598-024-82915-5

    ( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
    Figure Legend Snippet: ( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.

    Techniques Used:

    Related Articles

    other:

    Article Title: Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact
    Article Snippet: The ‘’Univariate feature ranking for classification using chi-square tests’’ technique, available in Matlab 2023a (i.e., fscchi2), was used to produce feature scores presented in Fig. a.



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    Image Search Results


    ( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.

    Journal: Scientific Reports

    Article Title: Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact

    doi: 10.1038/s41598-024-82915-5

    Figure Lengend Snippet: ( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.

    Article Snippet: The ‘’Univariate feature ranking for classification using chi-square tests’’ technique, available in Matlab 2023a (i.e., fscchi2), was used to produce feature scores presented in Fig. a.

    Techniques: