Supervised feature selection algorithm via discriminative ridge regression

Zhang, Shichao; Cheng, Debo; Hu, Rongyao; Deng, Zhenyun

doi:10.1007/s11280-017-0502-9

Cited by 20 publications

(9 citation statements)

References 42 publications

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“…Ridge. is defined as a biased estimate model to evaluate the error between feature variables and class label information [36]. Based on [37], suppose that X = [x 1 , ..., x n ] ∈ R d * n represents the original dataset that contains n ddimensional instances and belongs to c clusters.…”

Section: Embedded Methodsmentioning

confidence: 99%

Risk Factors of Breast Cancer Determination: a Comparative Study on Different Feature Selection Techniques

Maalej

Rejab

Nouira

2022

Preprint

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Purpose: Choosing the relevant features is important to provide a better understanding of the data and improve the prediction performance. Thus, the main aim of this paper is to identify the risk factors of breast cancer. Methods: focusing on two different datasets: Breast Cancer Surveillance Consortium (BCSC) and Breast Cancer Coimbra (BCC), we perform a comparative study of various feature selection methods: Filter Methods, Wrapper Methods and Embedded Methods. In addition, this work investigates the stability of these techniques when perturbation on datasets is added. Artficial Neural Network, Random Forest, SVM, Logistic Regression and Decision Tree are used for classification. Results: The results are compared when using all the features and when using only the top ranked. The classification performances are comparable in either cases. Furthermore, we found that invasive, glucose, resistin, insulin, leptin, age, adiponectin, BMI and HOMA are the most relevant features that promote breast cancer. Conclusion: Our findings demonstrate that the identified feature selection methods can efficiently determine the risk factors of breast cancer.

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Section: Embedded Methodsmentioning

confidence: 99%

Risk Factors of Breast Cancer Determination: a Comparative Study on Different Feature Selection Techniques

Maalej

Rejab

Nouira

2022

Preprint

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“…The most restrictive one is the missing completely at random (MCAR), such as Zhang (2008b, Zhang et al ( , 2016, Zhang (2002a, 2002b). The less restrictive one is the MAR (missing at random), such as Zhang (2008a), Qin et al (2007) and Zhang et al (2017aZhang et al ( , 2017bZhang et al ( , 2018aZhang et al ( , 2018bZhang et al ( , 2018cZhang et al ( , 2010Zhang et al ( , 2005. The unrestrictive one is the missing not at random (MNAR).…”

Section: Missing Data Mechanismsmentioning

confidence: 99%

Estimating equations under IPW imputation of missing data

Chen

2021

IJRIS

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The inverse probability weighted (IPW) imputation method is first applied to compensate for non-response. And then, the empirical likelihood (EL) inference is made for estimation equation parameters. It is a nice result to be obtained in this paper that the limiting distributions of the EL statistics are χ 2 -type distributions under the IPW imputation. Compared with the usually-used methods, the proposed method is easier to complement and performs more efficient.

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“…Wrappers usually run much slower than filter methods due to their consideration of inter-feature relationships [29]. Embedded methods [30][31][32][33] use a classification learning algorithm to evaluate the validity of features, which retain the high precision of the wrapper methods and have the high efficiency of filter methods. However, the time complexity is relatively high when processing high-dimensional data, and the redundant features cannot be completely removed [34].…”

Section: Introductionmentioning

confidence: 99%

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Song

Yao

et al. 2022

PLoS ONE

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Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.

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Supervised feature selection algorithm via discriminative ridge regression

Cited by 20 publications

References 42 publications

Risk Factors of Breast Cancer Determination: a Comparative Study on Different Feature Selection Techniques

Risk Factors of Breast Cancer Determination: a Comparative Study on Different Feature Selection Techniques

Estimating equations under IPW imputation of missing data

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

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