A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification

Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

doi:10.1007/s11634-018-0334-1

Cited by 56 publications

(26 citation statements)

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“…Filtering methods, which reduce dimensionality and try to retain the most promising features as possible, have long been under development. A number of filtering methods has been proposed to rank features, such as Information gain [13], Markov blanket [14], Bayesian variable selection [15], Boruta [16], Fisher score [17], Relief [18], maximum relevance and minimum redundancy (MRMR) [19], marginal maximum likelihood score (MMLs) [20], among which MMLS is one of the simplest and computationally efficient methods of feature selection with some criteria.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Kim

2019

Mathematics

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Over the last decade, high dimensional data have been popularly paid attention to in bioinformatics. These data increase the likelihood of detecting the most promising novel information. However, there are limitations of high-performance computing and overfitting issues. To overcome the issues, alternative strategies need to be explored for the detection of true important features. A two-stage approach, filtering and variable selection steps, has been receiving attention. Filtering methods are divided into two categories of individual ranking and feature subset selection methods. Both have issues with the lack of consideration for joint correlation among features and computing time of an NP-hard problem. Therefore, we proposed a new filter ranking method (PF) using the elastic net penalty with sure independence screening (SIS) based on resampling technique to overcome these issues. We demonstrated that SIS-LASSO, SIS-MCP, and SIS-SCAD with the proposed filtering method achieved superior performance of not only accuracy, AUROC, and geometric mean but also true positive detection compared to those with the marginal maximum likelihood ranking method (MMLR) through extensive simulation studies. In addition, we applied it in a real application of colon and lung cancer gene expression data to investigate the classification performance and power of detecting true genes associated with colon and lung cancer.

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Section: Introductionmentioning

confidence: 99%

“…[21] utilized the Relief statistical method to rank features. [20] gave a marginal maximum likelihood estimator as a feature ranking method and improved classification accuracy. [22] also developed a novel method to rank features and then chose the optimal subset of features.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Kim

2019

Mathematics

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“…This approach has been applied only on two binary‐class data sets, which can be considered as a limitation. Algamal and Lee [38] proposed a two‐stage sparse logistic regression for an efficient gene selection and cancer classification. Experimental results show that the suggested method significantly outperforms other existing techniques in terms of CA, AUC, and G‐mean.…”

Section: State‐of‐the‐art Techniquementioning

confidence: 99%

“…The training time and testing time taken by various algorithms are calculated in terms of seconds. It is observed from the table that, for almost all data sets, the execution time by the proposed method is much less than that of the existing [40] 91.18 90 100 BDE-XRankf [29] 82.4 75 95 8-S PMSO [33] 98.1 94.2 -IRLDA [41] 97 --GEM [25] 91.5 91.2 -AEN-CMI [37] 91.05 89.30 -SLR [38] 95.51 94.61 -DFS [59] 98 The bold fonts indicate the highest results and the name of the proposed techniques. methods.…”

Section: Runtime Analysismentioning

confidence: 99%

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Baliarsingh

Vipsita

2020

IET syst. biol.

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“…The problem of overdispersion usually occurs in count data. Unlike Poisson regression model, negative binomial regression can handle the overdispersion issue [5,6,31].…”

Section: Introductionmentioning

confidence: 99%

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Algamal

2019

Stat., optim. inf. comput.

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Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Count data regression modeling has received much attention in several science fields in which the Poisson and negative binomial regression models are the most basic models. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, firefly algorithm is proposed to perform variable selection for count data regression models. Extensive simulation studies and two real data applications are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.

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A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification

Cited by 56 publications

References 50 publications

Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Variable Selection in Count Data Regression Model based on Firefly Algorithm

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