• A Hybrid Feature Selection Method Based on Binary Differential ...🔍
• Feature importance in neural networks🔍
• Enhancing Big Data Feature Selection Using a Hybrid Correlation ...🔍
• Feature Selection Based on BP Neural Network and Adaptive ...🔍
• Hybrid deep learning approach to improve classification of low ...🔍
• When should we perform feature selection before running deep ...🔍
• Hybrid Neural Network Models for Postprocessing Medium|Range ...🔍
• Why do neural networks need feature selection / engineering?🔍

A Hybrid Feature Selection Method Based on Binary Differential ...

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and ...

Feature importance in neural networks - Data Science Stack Exchange

That will give you an idea on the contribution of a variable. Alternatively, stick with importance scores of Tree-based models (such as Random ...

Enhancing Big Data Feature Selection Using a Hybrid Correlation ...

Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network ( ...

Feature Selection Based on BP Neural Network and Adaptive ...

Feature selection can classify the data with irrelevant features and improve the accuracy of data classification in pattern classification.

Hybrid deep learning approach to improve classification of low ...

domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning ... model varies across ...

When should we perform feature selection before running deep ...

Thus, feature selection is a critical process in developing deep learning-based systems. It helps ensure you have a simple network that won't be ...

Hybrid Neural Network Models for Postprocessing Medium-Range ...

Feature selection is a process that seeks a subset of features/input variables that are relevant to a given problem for the construction of a machine learning ...

Why do neural networks need feature selection / engineering?

What if the "sufficiently deep" network is intractably huge, either making model training too expensive (AWS fees add up!) or because you ...

Recurrent Neural Network Based Feature Selection for High ...

If all the features are treated equally while performing machine learning (ML) such as classification on the data, it will degrade the ...

Embedded feature selection for neural networks via learnable drop ...

However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability ...

A Hybrid Approach for Feature Selection and Classification using ...

The evolutionary algorithm can be used to solve various types of optimization problems, such as feature selection and model selection. The ...

Hybridizing Artificial Neural Networks Through Feature Selection ...

NN-based hybrid model. where x denotes the feature space, y ... features with realistic initial weights would lead to more accurate neural network ...

Hybrid feature selection model based on machine learning and ...

Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor ...

A hybrid convolutional neural network approach for feature selection ...

All the experimental outcomes reveal that the KFS based CJaya-CNN model is outperforming. Hence, the presented method can be used as a ...

A Deep Learning-Based Hybrid Feature Selection Approach for ...

In this research, we developed a deep learning-based hybrid feature selection approach combing Sparse Autoencoder (SAE) and Logistic Regression-Recursive ...

How to decide which features to use in a neural network model

Manual selection: Selecting features based on domain knowledge or prior experience. This can be useful when working with a small number of ...

Rotating machinery flow field prediction based on hybrid neural ...

Compared with traditional machine learning models such as random forest model, hybrid neural network model takes up only 11.4% of memory, and ...

A Hybrid Method of Feature Selection and Neural Network with ...

A hybrid approach is proposed based on UTA algorithm and two-layer neural network, which are updated uses its weights genetic to enhance the prediction of ...

A Hybrid Model Based on Convolutional Neural Network and Long ...

In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning neural networks ...

Fusion of statistical importance for feature selection in Deep Neural ...

In this study, we aim to focus on enhancing the performance of DNN-based IDS by proposing a novel feature selection technique that selects features via fusion ...