A novel variable selection algorithm for multi-layer perceptron with elastic net

Zhang, Fangfang; Sun, Kai; Wu, Xiuliang

doi:10.1016/j.neucom.2019.04.091

Cited by 22 publications

(5 citation statements)

References 29 publications

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“…Support vector machines (SVM) 17 , core vector machines (CVM) 18 , random forest (RF) 19 , k-nearest neighbours (k-NN) 20 , multilayer perceptron (MLP) 21 and binary logistic regression (BLR) 22 was applied on each of the two datasets in order to perform a classification. SVM is a discriminative classifier that works by separating the input space into various dimensions by finding an optimised hyperplane.…”

Section: Methodsmentioning

confidence: 99%

The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

Singh

2021

Ann Dermatol

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Background Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy. However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. Objective This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. Methods Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. Results The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. Conclusion This study successfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy.

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Section: Methodsmentioning

confidence: 99%

The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

Singh

2021

Ann Dermatol

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“…The machine noise, light, and magnetic field produced by offshore wind farms will have a certain impact on the foraging, breeding, and migration of birds [26,27]. For example, the offshore wind farms may directly occupy the habitat of seabirds, thus affecting their nesting and reproduction.…”

Section: Sea-sky Monitoringmentioning

confidence: 99%

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Kou

Zhang

et al. 2022

Sensors

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In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

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“…A linear regression method called the least absolute shrinkage and selection operator (LASSO) and MLP configuration were integrated for embedded feature selection, and the feature selection procedure was repeatedly performed to improve the performance of feature selection [12]. In addition, the elastic net was applied to improve feature selection performance of LASSO-based method of [12] in [13]. In [32], the random forest model was integrated in a deep neural network, which has a large number of hidden layers between the input and output layers, to detect sparse feature representations from gene expression profiles while preventing overfitting.…”

Section: Related Workmentioning

confidence: 99%

“…The linear regression-based feature selection method with neural networks is one of the famous feature selection methods that can provide an interpretable and high-performance feature selection model [12], [13]. In this paper, we propose an embedded feature selection algorithm based on linear regression and neural network called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP).…”

Section: Introductionmentioning

confidence: 99%

Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron

Seo

Cho

2020

IEEE Access

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Gene expression profiling is a useful technique for analyzing cellular function, and gene expression profiles are widely studied in human cancer research. Gene expression data usually consist of a very large number of features and a relatively small number of samples, and extracting a small number of important features from these data is a major challenge of gene expression-based analysis in cancer research. In this paper, we propose an embedded feature selection algorithm using boosted linear regression-based feature selection. The boosting technique is applied to derive the ensemble feature selector and improve the performance of linear regression-based feature selection. The proposed feature selection algorithm, called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP), repeats the boosted feature selection process to extract the smallest feature subset while maintaining good classification performance. We apply the proposed BREG-MLP to some human cancer-related gene expression data sets for the purpose of extracting important features, and we confirm that BREG-MLP offers improved performance compared to single regression-based feature selection methods.

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A novel variable selection algorithm for multi-layer perceptron with elastic net

Cited by 22 publications

References 29 publications

The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron

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