Intrusion Detection System: An Automatic Machine Learning Algorithms Using Auto- WEKA

Samawi, Venus W.; Yousif, Suhad A.; Al-Saidi, Nadia M. G.

doi:10.1109/icsgrc55096.2022.9845166

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…However, the paper lacks a detailed explanation of the progressive sampling technique used, limiting the assessment of its effectiveness and potential limitations. It is worth noting that the auto-waikato environment for knowledge analysis (WEKA) approach [7] has a 30-hour time budget for each run, whereas the automatic model in this research averaged around 5 hours.…”

Section: Figure 1 Progressive Learning Curvementioning

confidence: 99%

Improving time efficiency in big data through progressive sampling-based classification model

Bangera,

Kayarvizhy,

Luharuka

et al. 2024

IJEECS

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<span>The proposed system aims to overcome challenges posed by large databases, data imbalance, heterogeneity, and multidimensionality through progressive sampling as a novel classification model. It leverages sampling techniques to enhance processing performance and overcome memory restrictions. The random forest regressor feature importance technique with the gini significance method is employed to identify important characteristics, reducing the data’s features for classification. The system utilizes diverse classifiers such as random forest, ensemble learning, support vector machine (SVM), k-nearest neighbors’ algorithm (KNN), and logistic regression, allowing flexibility in handling different data types and achieving high accuracy in classification tasks. By iteratively applying progressive sampling to the dataset with the best features, the proposed technique aims to significantly improve performance compared to using the entire dataset. This approach focuses computational resources on the most informative subsets of data, reducing time complexity. Results show that the system can achieve over 85% accuracy even with only 5-10% of the original data size, providing accurate predictions while reducing data processing requirements. In conclusion, the proposed system combines progressive sampling, feature selection using random forest regressor feature importance (RFRFI-PS), and a range of classifiers to address challenges in large databases and improve classification accuracy. It demonstrates promising results in accuracy and time complexity reduction.</span>

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Section: Figure 1 Progressive Learning Curvementioning

confidence: 99%

Improving time efficiency in big data through progressive sampling-based classification model

Bangera,

Kayarvizhy,

Luharuka

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…However, ML requires several steps to make a good model, such as choosing the best preprocessing steps, tuning the hyperparameters, and choosing the suitable algorithm [9] and [10]. Furthermore, because most healthcare professionals lack sufficient programming experience, AutoML solutions help build and enhance ML pipelines [11] and [12]. Furthermore, for AutoML, numerous frameworks are available [13] to tackle the above difficulties.…”

Section: Introductionmentioning

confidence: 99%

“…The standard SMAC optimization integrates both meta-learning and ensemble techniques [14]. Effective AutoML pipelines are made up of preprocessing steps and ML classifiers, chosen by using Auto-Sklearn, which employs meta-learning, Bayesian optimization, and ensemble selection [11][12][13]. In these studies, the authors used a dataset of blood tests to predict COVID-19.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection via Blood Tests using an Automated Machine Learning Tool (Auto-Sklearn)

Moatz,

Yousif

2023

Iraqi Journal of Science

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Widespread COVID-19 infections have sparked global attempts to contain the virus and eradicate it. Most researchers utilize machine learning (ML) algorithms to predict this virus. However, researchers face challenges, such as selecting the appropriate parameters and the best algorithm to achieve an accurate prediction. Therefore, an expert data scientist is needed. To overcome the need for data scientists and because some researchers have limited professionalism in data analysis, this study concerns developing a COVID-19 detection system using automated ML (AutoML) tools to detect infected patients. A blood test dataset that has 111 variables and 5644 cases was used. The model is built with three experiments using Python's Auto-Sklearn tool. First, an analysis of the Auto-Sklearn process is done by studying the impact of several learning settings and parameters on the COVID-19 dataset using different classification methods, namely meta-learning, ensemble learning, and a combination of ensemble learning and meta-learning. The results show that using Auto-Sklearn with a meta-learning and ensemble learning parameter model predicts the patients infected with COVID-19 with high accuracy, reaching 96%. Furthermore, the best algorithm selected is the Random Forest Classifier (RF), which outperforms other classification methods. Finally, AutoML can assist those new to data sciences or programming skills in selecting the appropriate algorithm and hyperparameters and reducing the number of steps required to achieve the best results.

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“…The utilization of this methodology results in the automated design of complex neural network architectures, thereby enhancing the efficacy and effectiveness of deep learning tasks. Several studies have utilized AutoML techniques such as Auto-Weka [13], Auto-Arima [14], Auto-RapidMiner [15], and many others.…”

Section: Introductionmentioning

confidence: 99%

AutoKeras for Fake News Identification in Arabic: Leveraging Deep Learning with an Extensive Dataset

S. Matti,

A. Yousif

2023

ANJS

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Social media and the World Wide Web have led to a worrying rise in spreading false information, which presents a significant worldwide issue. Identifying and preventing false information is crucial in promoting an informed and knowledgeable society. The identification of false information, specifically in the Arabic dialect, presents inherent difficulties due to its diverse characteristics and linguistic intricacies. This study implements AutoKeras, a deep learning-based machine learning framework. Using advanced optimization techniques, the neural network architecture search, hyperparameter adjustments, and model selection can all be automated in AutoKeras. Therefore, it is suitable for our fake news detection task. The methodology employs proficient deep learning algorithms and natural language processing methods to acquire distinct characteristics that enable accurate differentiation between genuine and fake news. The present study uses various sources, including news websites, social media platforms, and blogs, to construct the dataset. The AutoKeras-based approach is superior to multiple state-of-the-art approaches to detecting fabricated news in Arabic, as evidenced by the experimental results. The suggested method outperforms 93.2% accuracy in identifying fake news, demonstrating its superior efficacy. This demonstrates the great promise of the deep learning-based Auto model for detecting false information.

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Intrusion Detection System: An Automatic Machine Learning Algorithms Using Auto- WEKA

Cited by 7 publications

References 16 publications

Improving time efficiency in big data through progressive sampling-based classification model

Improving time efficiency in big data through progressive sampling-based classification model

COVID-19 Detection via Blood Tests using an Automated Machine Learning Tool (Auto-Sklearn)

AutoKeras for Fake News Identification in Arabic: Leveraging Deep Learning with an Extensive Dataset

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