AdaBoost Algorithm with Single Weak Classifier in Network Intrusion Detection

Natesan, Prathiba; Balasubramanie, P.; Gowrison, G.

doi:10.4018/978-1-4666-8761-5.ch011

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…AdaBoost can be used for both binary and multiclass classification. The algorithm can be formalized in both standard and original forms for both binary and multiclass classification [51,52]. Decision Tree (DT) classifier is typically used as the base estimator, and the corresponding train and run time complexity are O(T�Q�N) and O(T), respectively, where T is the number of base estimators.…”

Section: Adaptive Boosting (Adaboost)mentioning

confidence: 99%

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Hemakom,

Atiwiwat,

Israsena

2023

PLoS ONE

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Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.

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Section: Adaptive Boosting (Adaboost)mentioning

confidence: 99%

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Hemakom,

Atiwiwat,

Israsena

2023

PLoS ONE

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“…63 An experimental comparison of the performance of decision trees against Naive Bayes and Bayes Net as base classifiers is presented in. 64 The authors reported that AdaBoost with decision trees as base classifier achieved the highest classification rate with lowest computational time.…”

Section: The Learning Algorithmmentioning

confidence: 99%

Automated Resolution Selection for Image Segmentation

Al-Qunaieer¹,

Tizhoosh²,

Rahnamayan³

2016

Preprint

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It is well-known in image processing in general, and hence in image segmentation in particular, that computational cost increases rapidly with the number and dimensions of the images to be processed. Several fields, such as astronomy, remote sensing, and medical imaging, routinely use numerous very large images, which might also be 3D and/or captured at several frequency bands, all adding to the computational expense. Multiresolution analysis is a method of increasing the efficiency of the segmentation process. One multiresolution approach is the coarse-to-fine segmentation strategy, whereby the segmentation starts at a coarse resolution and is then fine-tuned during subsequent steps. The starting resolution for segmentation is generally selected arbitrarily with no clear selection criteria. The research reported in this paper showed that starting from different resolutions for image segmentation results in different accuracies and computational times, even for images of the same category (depicting similar scenes or objects). An automated method for resolution selection for an input image would thus be beneficial. This paper introduces a framework for the automated selection of the best resolution for image segmentation. We propose a measure for defining the best resolution based on user/system criteria, offering a trade-off between accuracy and computation time. A learning approach is then introduced for the selection of the resolution, whereby extracted image features are mapped to the previously determined best resolution. In the learning process, class (i.e., resolution) distribution is generally imbalanced, making effective learning from the data difficult. A variant of AdaBoost, called RAMOBoost, is therefore used in this research. Local Binary Patterns (LBP) features are utilized in this research. Experiments conducted with three datasets using two different segmentation algorithms show that the resolutions selected through learning enable much faster segmentation than the original ones, while retaining at least the original accuracy. For two of the three datasets used, the segmentation results obtained with the proposed framework were significantly better than with the original resolution with respect to both accuracy and time.

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“…Sun et al compared Discrete, Real, and Gentle AdaBoost by analyzing the experimental results in license plate detection and explained that Gentle AdaBoost achieves better performance than the other two methods [51]. Comparison in [52] focused on comparing weak classifiers of AdaBoost constructed by Bayes net, naive Bayes, and decision trees, and it showed that decision trees are the best. A review systematically introduced AdaBoost variants proposed during 1999 to 2012.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression Trees

Nagahashi

2015

Journal of Electrical and Computer Engineering

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As a machine learning method, AdaBoost is widely applied to data classification and object detection because of its robustness and efficiency. AdaBoost constructs a global and optimal combination of weak classifiers based on a sample reweighting. It is known that this kind of combination improves the classification performance tremendously. As the popularity of AdaBoost increases, many variants have been proposed to improve the performance of AdaBoost. Then, a lot of comparison and review studies for AdaBoost variants have also been published. Some researchers compared different AdaBoost variants by experiments in their own fields, and others reviewed various AdaBoost variants by basically introducing these algorithms. However, there is a lack of mathematical analysis of the generalization abilities for different AdaBoost variants. In this paper, we analyze the generalization abilities of six AdaBoost variants in terms of classification margins. The six compared variants are Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, Parameterized AdaBoost, Margin-pruning Boost, and Penalized AdaBoost. Finally, we use experiments to verify our analyses.

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AdaBoost Algorithm with Single Weak Classifier in Network Intrusion Detection

Cited by 4 publications

References 6 publications

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Automated Resolution Selection for Image Segmentation

Analysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression Trees

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