Hyperparameters of Multilayer Perceptron with Normal Distributed Weights

Karaki, Youmna Shawki; Ivanov, Nick

doi:10.1134/s1054661820020054

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The parameter random_state is used with its value set to 50 to determine a random number of generations for bias initialization and weights. The parameter max_iter is also implemented with the value of 200 to indicate the maximum number of iterations or to regulate the use of each data point [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

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Section: Methodsmentioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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“…Neurons in MLP are trained with a backpropagation algorithm. [15] MLP is commonly used for classification, recognition, prediction, and forecasting activities.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction

Amalia

Deborah

Yulita

2022

Sinergi

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The airline business is one of the businesses determined by the quality of its services. Every airline creates its best service so that customers feel satisfied and loyal to using their services. Therefore, customer satisfaction is an essential metric to measure features and services provided. By having a database on customer satisfaction, the company can utilize the data for machine learning modelling. The model generated can predict customer satisfaction by looking at the existing feature criteria and becoming a decision support system for management. This article compares machine learning between Split Point and Attribute Reduced Classifier (SPAARC), Multilayer Perceptron (MLP), and Random Fores (RF) in predicting customer satisfaction. Based on the data testing, the Random Forest algorithm provides better results with the lowest training time compared to SPAARC and MLP. It has an accuracy of 95.827%, an F-score of 0.958, and a training time of 84.53 seconds.

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“…Neural networks are a field of science that produces various kinds of renewable technologies such as pattern recognition, big data classification, to cars that can drive themselves automatically [11]. Neural networks have the advantage that the solution is continuous so that it is easy to understand and process, and does not require modification methods to solve a complex problem [12].…”

Section: Introductionmentioning

confidence: 99%

Room occupancy classification using multilayer perceptron

Wijaya¹,

Aulia

Jumanto³

et al. 2021

joscex

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A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy .

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Hyperparameters of Multilayer Perceptron with Normal Distributed Weights

Cited by 7 publications

References 10 publications

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction

Room occupancy classification using multilayer perceptron

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