Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

doi:10.22237/jmasm/1430453640

Cited by 28 publications

(19 citation statements)

References 15 publications

(19 reference statements)

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“…The lasso penalized function has attracted a lot of attention because of its capability to yield sparse solution and its great performance in classification . However, it was proved in Zou that the lasso does not exhibit as consistent variable selection method.…”

Section: Methodsmentioning

confidence: 99%

“…The lasso penalized function has attracted a lot of attention because of its capability to yield sparse solution and its great performance in classification. [34][35][36][37][38] However, it was proved in Zou 30 that the lasso does not exhibit as consistent variable selection method. In other words, lasso lacks the oracle properties, which is defined that a method selects the correct subset of descriptors with probability approaching to 1, and estimates the nonzero descriptors as efficiently as would be possible if the irrelevant descriptors were known in advance.…”

Section: Penalized Logistic Regressionmentioning

confidence: 99%

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A novel molecular descriptor selection method inQSARclassification model based on weighted penalized logistic regression

Algamal

Lee

2017

Journal of Chemometrics

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Molecular descriptor selection is a pivotal tool for quantitative structure–activity relationship modeling. This paper proposes a novel molecular descriptor selection method on the basis of taking into account the information of the group type that the descriptor belongs to. This descriptor selection method is on the basis of combining penalized logistic regression with 2‐sample t test. The proposed method can perform filtering and weighting simultaneously. Specifically, 2‐sample t test is employed as filter method by removing the descriptor which is not show statistically significant difference. On the other hand, a weighted penalized logistic regression is used by assigning a weight depending on the 2‐sample t test value inside the descriptor type block. The proposed method is experimentally tested and compared with state‐of‐the‐art selection methods. The results show that our proposed method is simpler and faster with efficient classification performance.

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Section: Methodsmentioning

confidence: 99%

Section: Penalized Logistic Regressionmentioning

confidence: 99%

A novel molecular descriptor selection method inQSARclassification model based on weighted penalized logistic regression

Algamal

Lee

2017

Journal of Chemometrics

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“…4 must be modified. The conditional probability p of the system state is defined as follows: 31 Y is replaced by the “logit” transformation of the conditional probability p : …”

Section: Process Pattern and Elastic Net-principal Component Analysismentioning

confidence: 99%

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

2018

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This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.

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“…The classical linear model assumes that the mean of the response variable y is a linear function of a set of predictor variables [1][2][3][4][5][6][7], and that the response variable is continuous and normally distributed with constant variance. As a matter of fact, in many applications, the response variable is categorical or consists of counts or is continuous but non normal, so the ordinary least square method can't be applied to find the regression models [8][9][10][11][12][13][14][15]. Generalized linear models were introduced by Nelder and Wedderburn in 1972 [16] to address those limitations.…”

Section: Introductionmentioning

confidence: 99%

“…In the GLMs the mean of the response variable is modeled as a monotonic nonlinear transformation of a linear function of the predictor variables.The inverse of the transformation g is known as the link function. Many applications used GLM [15,[17][18][19][20][21][22][23]. An example of non normal continuous distribution that has many applications is the inverse Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Prediction of blood lead level in maternal and fetal us-ing generalized linear model

Algamal

Ali

2017

IJASP

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Over the past decades, with advanced data collection techniques, a different type of data continues to appear in various biological, sciences, medical, social, and economical studies. Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. Generalized linear models (GLMs) are generalization of the linear regression models, which allow fitting regression models to response variable that is non normal and follows a general exponential family. The aim of this study is to encourage and initiate the application of GLMs to predict the maternal and fetal blood-lead level. The inverse Gaussian distribution with inverse quadratic link function is considered. Four main effects were significant in the prediction of the maternal blood-lead level (pica, smoking of mother, dairy products intake of mother, calcium intake of mother), while in the prediction of the fetal blood-lead level, two main effects showed significance (dairy products intake of mother and hemoglobin of mother).

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Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Cited by 28 publications

References 15 publications

A novel molecular descriptor selection method inQSARclassification model based on weighted penalized logistic regression

A novel molecular descriptor selection method inQSARclassification model based on weighted penalized logistic regression

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

Prediction of blood lead level in maternal and fetal us-ing generalized linear model

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