SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification

Jiang, Weili; Chen, Zhenhua; Xiang, Yan; Shao, Dangguo; Ma, Lei; Zhang, Junpeng

doi:10.1109/access.2019.2933262

Cited by 26 publications

(9 citation statements)

References 65 publications

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“…Therefore, to detect anomalies in the complex interaction of various behaviors, an ensemble model using various models that are not dependent on one model is appropriate [19]. Methodologies such as voting, bagging, boosting, and stacking can be largely used to construct an ensemble model for detecting attacks and abnormal behaviors on the network [20]. This section describes six classification models frequently used as network anomaly detection models and explains why these models are appropriate as internal models of ensemble models for anomalous behavior detection.…”

Section: Noise Reduction Methods For Anomaly Detectionmentioning

confidence: 99%

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

2022

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As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous alteration of a system and network environments. Although machine learning is performed and evaluated using a generalized data set, its performance is expected to be similar in that specific network environment only. In this study, we propose a high-speed outlier detection method for a network dataset to customize the dataset in real-time for a continuously changing network environment. The proposed method uses an ensemble-based noise data filtering model using the voting results of 6 classifiers (decision tree, random forest, support vector machine, naive Bayes, k-nearest neighbors, and logistic regression) to reflect the distribution and various environmental characteristics of datasets. Moreover, to prove the performance of the proposed method, we experimented with the accuracy of attack detection by gradually reducing the noise data in the time series dataset. As a result of the experiment, the proposed method maintains a training dataset of a size capable of semi-real-time learning, which is 10% of the total training dataset, and at the same time, shows the same level of accuracy as a detection model using a large training dataset. The improved research results would be the basis for automatic tuning of network datasets and machine learning that can be applied to special-purpose environments and devices such as ICS environments.

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Section: Noise Reduction Methods For Anomaly Detectionmentioning

confidence: 99%

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

2022

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“…They also perform exceptionally well when the data contains both quantitative and categorical variables [47,48]. Accuracy (ACC), Precision, Recall, F1-score, and Area Under the Curve (AUC) are commonly used metrics for classification problems [49]. The accuracy of the model depicts its ability to perform the entire classification task.…”

Section: Exploratory Analysismentioning

confidence: 99%

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

Parthasarathy¹,

Raj²

2023

Computer Systems Science and Engineering

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For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago's child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model's execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.

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“…The better predictive accuracy is proportional to different model characteristics, which is the basic idea behind heterogenous ensemble. Combining different learning algorithm improves results as compared to a single base learner [31]. Since each algorithm has an entirely different prediction approach and therefore has different result accuracy.…”

Section: Approachmentioning

confidence: 99%

Lamb wave damage severity estimation using ensemble-based machine learning method with separate model network

Rizvi¹,

Abbas²

2021

Smart Mater. Struct.

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Lamb wave-based damage estimation have great potential for structural health monitoring. However, designing a generalizable model that predicts accurate and reliable damage quantification result is still a practice challenge due to complex behavior of waves with different damage severities. In the recent years, machine learning (ML) algorithms have been proven to be an efficient tool to analyze damage-modulated Lamb wave signals. In this study, ensemble-based ML algorithms are employed to develop a generalizable crack quantification model for thin metallic plates. For this, the scattering of Lamb wave signals due to different configuration of crack dimension and orientation is extensively studied. Various finite element simulations signals, representing distinct crack severities in term of crack length, penetration and orientation are acquired. Realizing that both temporal and spectral information of signal is extremely important to damage quantification, three time-frequency (TF) based damage sensitive indices namely energy concentration, TF flux and coefficient of energy variance are proposed. These damage features are extracted by employing smoothed-pseudo Wigner–Ville distribution. After that data augmentation technique based on the spline-based interpolation is applied to enhance the size of the dataset. Eventually, these fully developed damage dataset is deployed to train ensemble-based models. Here we propose separate model network, in which different models are trained and then link together to predict new and unseen datasets. The performance of the proposed model is demonstrated by two cases: first simulated data incorporated with high artificial noises are employed to test the model and in the second scenario, experimental data in raw form are used. Results indicate that the proposed model has the potential to develop a general model that yields reliable answer for crack quantification.

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SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification

Cited by 26 publications

References 65 publications

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

Lamb wave damage severity estimation using ensemble-based machine learning method with separate model network

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