Chaudhari et al. [16] proposed an enhanced form of the K-nearest neighbor (KNN) rule. To obtain synthetic samples, they employed counting filters for effectively skewed data, the Euclidean distance for estimating the distance between its neighbors, and the best mean value from the KNN for each...
Adam combines the advantages of two other extensions of stochastic gradient descent, namely Adaptive Gradient Algorithm (AdaGrad) and Root Mean Square Propagation (RMSProp), making it well-suited for our dataset with its high-dimensional nature. It has been shown in Eq. 7. [Math Processing Error...
To overcome the aforementioned challenges, we apply Deep Reinforcement Learning (DRL) to the problem of weakly-supervised anomaly detection in business process data. The proposed DRL approach is designed to exploit the small set of labeled anomalous data and explore the large set of unlabeled data,...
For the optimizer, Adaptive Moment Estimation (ADAM) [66], Nesterov-accelerated Adaptive Moment Estimation (NADAM) [67], and Root Mean Square Propagation (RMSProp) [68] were used. For the BioBERT model, we used an existing pre-trained contextualized word embedding, BiomedNLP-PubMedBERT, which...
For almost all ensemble methods, a series of models must first be created as basic models (or sub-model) to form an ensemble model. This means that several models are taught using training data. The baseline models are constructed of five different deep neural networks. Models in Keras are ...
[41]. The main drawback of the time series forecasting model is that it merely captures linear relationships. In addition, TS models require the input data to be stationary (whether in its raw form or as differenced data). Unfortunately, authors in [55] performed the popular Kwiatkowski-...
advantages of Adagrad and RMSprop, which scale the gradient by the square root of the accumulative square gradient to achieve fast convergence. Adam has become the default optimization algorithm for many DNNs due to its rapid convergence [26]. However, due to the sensitivity to initialization and...
The optimizer is RMSprop, lr = 0.01, rho = 0.9. Figure 14 Structure of surrogate model Full size image 4.4 Process of Optimization The specific optimization process is shown in Figure 15. Minimizing the values of 5 objectives is the ultimate goal of optimization. Figure 15 Process ...
Replacing RMSProp with adam, although gives an improvement over the original baseline, it still underperforms our method. Our approach gives state-of-the-art results, with an improvement of ∼3% F1 over the original baseline and an improvement of 1.88% F1 over the re-implemented baseline. ...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cog...