Advanced Data Analytics Using Python

Mukhopadhyay, Sayan

doi:10.1007/978-1-4842-3450-1

Cited by 7 publications

(7 citation statements)

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“…Although XGBoost has many hyperparameters in Table 1, this study only focuses on those that have been shown to significantly impact model performance in previous studies. The hyperparameters "subsample," "learning_rate," "max_leaves," "gamma," "max_depth," "colsample_bytree," and "min_child_weight" are used in this study, while the remaining hyperparameters are set to their default values in Python [27]. The ratio of training instances that are randomly selected for fitting each individual tree.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.</p>

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Section: Backgrounds and Related Workmentioning

confidence: 99%

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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“…In the last step of the back-propagation process, we calculated d(C)/d(W 1 ), layer one weights responsible for generating errors in the results of the output layer. (1) where…”

Section: D(c) D(w 3 )mentioning

confidence: 99%

“…For deep learning (DL) algorithms, the proper m-learning dataset is a crucial element for creating the right m-learning model [1]. DL algorithms are based on a collection of statistical and machine learning (ML) techniques used to find feature orders, weights, and their relationships often based on deep neural networks [2].…”

Section: Introductionmentioning

confidence: 99%

Deep neural network based m-learning model for predicting mobile learners’ performance

Adnan¹,

Habib²,

Ashraf³

et al. 2020

Turk J Elec Eng & Comp Sci

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The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

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“…Recently, some algorithms, such as grid search (GS), random search (RS), and genetic algorithm (GA), have been applied to determine the hyperparameters of the DL model, among which the GS algorithm was widely applied [14][15]. In addition, the characteristics of the input data are also important factors affecting the accuracy of the DL model.…”

Section: Introductionmentioning

confidence: 99%

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

Ngoc

Đại

Minh

2022

Adv. technol. innov.

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This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning (DL) models. The considered DL models are the convolutional neural network (CNN) and long short-term memory network (LSTMN). The procedure is made over (i) setting the configuration for CNN and LSTMN, (ii) establishing the hyperparameter values of CNN and LSTMN models based on epoch, batch, optimizer, dropout, filters, and kernel, (iii) using eight data standardization methods to standardize the input data, and (iv) using the GS algorithm to search the optimal hyperparameters based on the mean absolute error (MAE) and mean absolute percent error (MAPE) indexes. The effectiveness of the proposed method is verified on the power load data of the Australian state of Queensland and Vietnamese Ho Chi Minh city. The simulation results show that the proposed data standardization methods are appropriate, except for the zero-mean and min-max methods.

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Advanced Data Analytics Using Python

Cited by 7 publications

References 0 publications

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Deep neural network based m-learning model for predicting mobile learners’ performance

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

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