Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization
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TLDR
The proposed method can find the best hyperparameters for the widely used machine learning models, such as the random forest algorithm and the neural networks, even multi-grained cascade forest under the consideration of time cost.About:
This article is published in Journal of Electronic Science and Technology.The article was published on 2019-03-01 and is currently open access. It has received 496 citations till now. The article focuses on the topics: Hyperparameter optimization & Bayesian optimization.read more
Citations
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COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.
Ferhat Ucar,Deniz Korkmaz +1 more
TL;DR: This study demonstrates an AI-based structure to outperform the existing studies and shows how fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.
Journal ArticleDOI
Review of swarm intelligence-based feature selection methods
TL;DR: A comparative analysis of different feature selection methods is presented, and a general categorization of these methods is performed, which shows the strengths and weaknesses of the different studied swarm intelligence-based feature selection Methods.
Journal ArticleDOI
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Moloud Abdar,Maryam Samami,Sajjad Dehghani Mahmoodabad,Thang Doan,Bogdan Mazoure,Reza Hashemifesharaki,Li Liu,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +12 more
TL;DR: Wang et al. as mentioned in this paper applied three uncertainty quantification methods to deal with uncertainty during skin cancer image classification, i.e., Monte Carlo (MC), Ensemble MC (EMC) and Deep Ensemble (DE).
Journal ArticleDOI
An optimized XGBoost based diagnostic system for effective prediction of heart disease
TL;DR: A diagnostic system that utilizes an optimized XGBoost (Extreme Gradient Boosting) classifier to predict heart disease and results indicate that the proposed method could be used reliably to Predict heart disease in the clinic.
Journal ArticleDOI
A novel community detection based genetic algorithm for feature selection
TL;DR: In this paper, the authors proposed a genetic algorithm based on community detection, which functions in three steps, where feature similarities are calculated in the first step and features are classified by community detection algorithms into clusters throughout the second step In the third step, features are picked by a GA with a new community-based repair operation.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Random search for hyper-parameter optimization
James Bergstra,Yoshua Bengio +1 more
TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
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A neural probabilistic language model
TL;DR: The authors propose to learn a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences, which can be expressed in terms of these representations.