A multi‐cell multi‐objective self‐optimisation methodology based on genetic algorithms for wireless cellular networks

Sánchez–González, Juan; Sallent, O.; Pérez‐Romero, Jordi; Agustí, R.

doi:10.1002/nem.1831

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The author of Reference 50 used GAs to look for the different parameters of the model in a more systematic way. A system based on GA and a popular multi‐cell multi‐objective self‐optimization methodology was proposed in Reference 14.…”

Section: Methodsmentioning

confidence: 99%

“…True positive rate: TPR = TP (TP + FN) (11) True negative rate: TNR = TN (FP + TN) (12) False negative rate: FNR = FN (TP + FN) (13) False positive rate: FPR = FP (FP + TN) (14) In cases where the number of clicks is much lower than that of the legitimate clicks and the data set is highly imbalanced, the model accuracy can be misleading. Therefore, we used an uncertainty matrix to measure the quality of our model.…”

Section: Ucb-lstm-ga Model For Ctr Predictionmentioning

confidence: 99%

“…It is, therefore, necessary to use the feature selection approach and hyper‐parameter optimization technique to accurately differentiate customers. The selection of features and hyperparameters may be used to pick up the most important features, such as the window size and the number of hidden units to boost classification accuracy, 13,14 delete obsolete and redundant features, 15 resolve the dimensionality curse, 16 and minimize computational costs, 17 to simplify the models. This reduces the importance of the feature loss in the dataset 17 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Upper confidence bound integrated genetic algorithm‐optimized long short‐term memory network for click‐through rate prediction

Souissi

Ghorbel

2022

Appl Stoch Models Bus & Ind

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Data on online advertising is rising rapidly due to the fast development of science and technology. Click-through rate (CTR) prediction has become a critical task regarding the digital advertising industry and a key element in increasing advertising profits and user experience. Therefore, this article describes the problem of CTR prediction as a function of sequence classification tasks. Then, we proposed a novel optimization strategy to solve the high-dimensional problem and find a subset of relevant variables to ensure high performance of our model and maximize the number of clicks. Here, we introduced a feature selection and hyper-parameter optimization approach using genetic algorithms (GA) and the upper confidence bound (UCB) model to optimize micro-targeting technology, along with the long short-term memory (LSTM) network-based CTR prediction model. The efficiency of the proposed UCB-LSTM-GA model and two hybrid models, namely LSTM-GA and LSTM-PSO, is evaluated by comparing them to each other and to other machine-learning-based classification methods, including LSTM using a UCB algorithm (UCB-LSTM), High-order Attentive Factorization Machine (HoAFM), genetic algorithm-artificial neural network (GA-ANN), and a feature interaction graph neural network model (Fi-GNN). Our solution achieved as high as 87%, 89%, and 92% for respectively accuracy, precision, and recall, using the popular python tools with real Avazu datasets.

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Section: Methodsmentioning

confidence: 99%