Expanding the Narendra‐Fukunaga algorithm for selecting feature subset in classification problem

Duc, Tran Trong; Andrianasolo, Haja

doi:10.1080/10106049809354645

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The extended BB FSS algorithm (Duc & Andrianasolo, 1998), with the exception of the functionality of determining variables to be disregarded in the search process, was implemented in the Python programming language using the Mahalanobis distance as the subset evaluation metric. This algorithm, despite performing a complete FSS without exhausting every feature combination, may imply a high computational cost since both the number of evaluated subsets and dimension of the covariance matrix to be inverted during the Mahalanobis distance calculation grow exponentially as a function of the number of features (Webb, 2002).…”

Section: Methodsmentioning

confidence: 99%

Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

Carafini

Sacco

Vieira

2019

PeerJ

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BackgroundPelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women.MethodsThe available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression.ResultsOf the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.

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Section: Methodsmentioning

confidence: 99%

Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

Carafini

Sacco

Vieira

2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…Two FSS algorithms were tested: extended branch and bound algorithm (BB) and recursive feature elimination algorithm (RFE). The extended BB FSS algorithm [27], with the exception of the functionality of determining variables to be disregarded in the search process, was implemented in the Python programming language using the Mahalanobis distance as the subset evaluation metric. This algorithm, despite performing a complete FSS without exhausting every feature combination, may imply a high computational cost since both the number of evaluated subsets and dimension of the covariance matrix to be inverted during the Mahalanobis distance calculation grow exponentially as a function of the number of features [5].…”

Section: Feature Selectionmentioning

confidence: 99%

Peer Review #1 of "Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection (v0.2)"

2019

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Background. Pelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women. Methods. The available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression. Results. Of the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.

show abstract

Expanding the Narendra‐Fukunaga algorithm for selecting feature subset in classification problem

Cited by 2 publications

References 8 publications

Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

Peer Review #1 of "Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection (v0.2)"

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