Deep-FS: A feature selection algorithm for Deep Boltzmann Machines
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Summary
A novel algorithm, Deep Feature Selection (Deep-FS), which is capable of removing irrelevant features from large datasets in order to reduce the number of inputs which are modelled during the learning process and overcomes the main limitations of classical feature selection algorithms.
- Type
- article
- Published
- 2018-12-01
- Cited by
- 75
- References
- 66
- Access
- Open access
- OpenAlex
- https://openalex.org/W2892911634
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53046674
Keywords
Boltzmann machine, Restricted Boltzmann machine, Artificial intelligence, Deep learning, MNIST database
References
- Novel Maximum-Margin Training Algorithms for Supervised Neural Networks
- Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
- Multimodal learning with deep Boltzmann machines
- Deep Boltzmann Machines
- Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
- The Cancer Genome Atlas Pan-Cancer Analysis Project
- Toward Optimal Feature Selection
- Deep Learning Based Feature Selection for Remote Sensing Scene Classification
- A 3D model recognition mechanism based on deep Boltzmann machines
- Support vector machines in water quality management.
- Training restricted Boltzmann machines: An introduction
- Towards adaptive learning with improved convergence of deep belief networks on graphics processing units
- Robust Boltzmann Machines for recognition and denoising
- Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks
- Facial Expression Recognition Using Deep Boltzmann Machine from Thermal Infrared Images
- Classification using Markov blanket for feature selection
- A Bayesian Approach to Multimodal Visual Dictionary Learning
- A weighted least-squares approach to clusterwise regression
- The human splicing code reveals new insights into the genetic determinants of disease
- Dropout: a simple way to prevent neural networks from overfitting
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- A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System
- A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
- The Effect of Evidence Transfer on Latent Feature Relevance for Clustering
- Refining the features transferred from pre-trained inception architecture for aerial scene classification
- A new feature selection method based on task environments for controlling robots
- A review of learning in biologically plausible spiking neural networks
- Minor Constraint Disturbances for Deep Semi-supervised Learning
- Quantum in the Cloud: Application Potentials and Research Opportunities
- UCRDNet: Unsupervised Collaborative Representation Deep Network for Clustering
- Consistent feature selection for neural networks via Adaptive Group Lasso
- Feature selection of Thyroid disease using Deep Learning: A Literature survey
- Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
- Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection
- Representation learning via a semi-supervised stacked distance autoencoder for image classification
- Consistent Feature Selection for Analytic Deep Neural Networks
- A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data
- Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
- Effective Cancer Subtype and Stage Prediction via Dropfeature-DNNs
- Survey on Neural Network Architectures with Deep Learning
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