Weighted Mean Squared Deviation Feature Screening for Binary Features

Wang, Gaizhen; Guan, Guoyu

doi:10.3390/e22030335

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…However, the recent availability of more informative databases obtained from EHR opens up new research opportunities. These current databases containing records of hundreds of thousands of appointments allow the use of modern predictive techniques such as deep neural networks or novel binary classification algorithms for high-dimensional settings, such as [ 65 , 66 ]. A second research line consists of developing and incorporating strategies that reduce the negative effects of class imbalance.…”

Section: Discussionmentioning

confidence: 99%

Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

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Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

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Section: Discussionmentioning

confidence: 99%

Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

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show abstract

“…The SIS and ISIS are routinely being applied in ultra-high dimensional applications and have also been extended to more complex models. 11–20 However, one major drawback of the SIS or ISIS is their non-robust nature against data contamination as indicated already in the discussion of the original paper itself. This issue can be crucial when applying the method for screening of important genes from large-scale Omics data, which are often prone to at least a few outliers.…”

Section: Introductionmentioning

confidence: 99%

A robust variable screening procedure for ultra-high dimensional data

Ghosh

Thoresen

2021

Stat Methods Med Res

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Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and some pre-screening of the variables may be necessary. A number of procedures for such pre-screening has been developed; among them the Sure Independence Screening (SIS) enjoys some popularity. However, SIS is vulnerable to outliers in the data, and in particular in small samples this may lead to faulty inference. In this paper, we develop a new robust screening procedure. We build on the density power divergence (DPD) estimation approach and introduce DPD-SIS and its extension iterative DPD-SIS. We illustrate the behavior of the methods through extensive simulation studies and show that they are superior to both the original SIS and other robust methods when there are outliers in the data. Finally, we illustrate its use in a study on regulation of lipid metabolism.

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“…More specifically, by applying a certain condition, methods, or criteria to rank the features, then order them in a descending order based on the rank calculated while selecting the features highest in the order to represent the rest. The work in [7,8] and [9] are examples of filter methods to select features. On the other hand, wrapper methods relies on the continuous selection of various subsets of features from the feature space, and utilize them each to train a machine learning model and infer which subsets to choose and which to eliminate according to the resulting performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data

Mallak¹,

Fathi²

2020

Preprint

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Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.

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Weighted Mean Squared Deviation Feature Screening for Binary Features

Cited by 4 publications

References 18 publications

Patient No-Show Prediction: A Systematic Literature Review

Patient No-Show Prediction: A Systematic Literature Review

A robust variable screening procedure for ultra-high dimensional data

Unsupervised Feature Selection Using Recursive k-Means Silhouette Elimination (RkSE): A Two-Scenario Case Study for Fault Classification of High-Dimensional Sensor Data

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