Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information

Wang, Yadi; Yang, Xin-Guang; Lu, Yue

doi:10.1016/j.apm.2019.01.044

Cited by 47 publications

(18 citation statements)

References 35 publications

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“…It is the average performance resulting from 100 iterations. G50753, M63391, and M76378 were reported as significant genes related to colon cancer in [45]. M76378, H08393, H55916, M63391, R87126, and T47377 were also reported as genes associated with colon cancer in [46].…”

Section: Real Data Analysismentioning

confidence: 99%

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: Real Data Analysismentioning

confidence: 99%

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

Kim

2019

Mathematics

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“…Therefore, further work is needed. However, we believe that our proposed approach complements existing sparse methods for EEG emotional data classification well (Wang et al, 2019), which will help researchers to better analyze such data.…”

Section: Discussionmentioning

confidence: 99%

Sparse Logistic Regression With L1/2 Penalty for Emotion Recognition in Electroencephalography Classification

Chen

Miao

Deng

et al. 2020

Front. Neuroinform.

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Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q-type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1 , Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.

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“…Wang et al [37] developed a method combining adaptive elastic net (AEN) with conditional mutual information (CMI) for microarray gene selection. The major advantage of this method is that it reduces the influence of the wrong initial estimation to gene selection and classification.…”

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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Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information

Cited by 47 publications

References 35 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

Sparse Logistic Regression With L1/2 Penalty for Emotion Recognition in Electroencephalography Classification

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

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