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Hybrid ensemble techniques used for classifier and feature selection in intrusion detection systems

INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2022年第4期28卷 389-413页

作者： Kharwar, Ankit Thakor, Devendra Uka Tarsadia Univ Chhotubhai Gopalbhai Patel Inst Technol Bardoli Gujarat India

The data security of networks is a universal problem for governments, companies, and persons. The frequency of internet attacks has grown substantially, as have attacker strategies. The solution to this problem is intrusion detection, a typical and successful methodology for planning intrusion detection systems (IDS) with machine learning. The proposed IDS method consists of three stages: pre-processing, feature selection, and classification. We remove duplicate data and normalised data in our method's first stage. Sequential forward floating selection (SFFS) with extra-tree use for feature selection removes unwanted features in our method's second stage. LogitBoost with extra-tree classification to use selected features in our method third stage. The proposed method is evaluated on standard datasets KDD CUP'99, NSL-KDD, UNSW-NB15, CICIDS2017, and CICIDS2018. The experimental results show that the proposed method outperforms the existing work in terms of accuracy, false alarm rate, and detection rate.

关键词： intrusion detection anomaly detection machine learning ensemble methods extra-tree feature selection sequential forward floating selection SFFS boosting algorithm LogitBoost algorithm network security

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A Detection Strategy of Multi-Pose Face in Compressed Domain

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Wuhan University Journal of Natural Sciences 2004年第5期9卷 845-850页

作者： CHENLei ZHOUGuo-fu SchoolofComputing.NationalUniversityofSingapore Singapore117513 StateTechnologyCenterofMultimediaSoftwareEngineering.WuhanUniversity.Wuhan430072 Hubei.China

In this paper, we present a strategy to implement multi-pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classificrs to distinguish faces and non-faces. Moreover, to get more accurate results of the face detection, we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers. Through comparing and analyzing results of some experiments on the synthetic data and the natural data, we can get more satisfied results by the strong classifiers than by the weak classifies. Key words weak classifier - boosting algorithm - face detection - compressed domain CLC number TP 391. 41 Foundation item: Supported by the National 863 Program (2002 AA11101) and Open Fund of State Technology Center of Multimedia Software Engineering (621-273128) Biography: CHEN Lei(1978-), male, Master, research direction: image process, image recognition and AI.

关键词： weak classifier boosting algorithm face detection compressed domain

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Machine Learning Regression algorithms for Shear Strength Prediction of SFRC-DBs: Performance Evaluation and Comparisons

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第4期49卷 4711-4727页

作者： Maabreh, Majdi Almasabha, Ghassan Hashemite Univ Fac Prince Al Hussein Bin Abdallah II Informat Tec Dept Informat Technol POB 330127 Zarqa 13133 Jordan Hashemite Univ Fac Engn Dept Civil Engn POB 330127 Zarqa 13133 Jordan

The objective of this study is to assess the shear strength of Deep Steel Fiber Reinforced Concrete Beams without stirrups (SFRC-DBs) and to forecast the values of such shear strengths using machine learning techniques. To facilitate the design of such complex structural elements, this work generated an up-to-date database of 170 SFRC-DBs, where eleven popular machine learning regression algorithms are evaluated using five common performance metrics. This introduces a comprehensive assessment framework enabling practitioners to develop reliable and efficient data-driven applications for this task. The evaluation process included the multilayer perceptron (MLP), the linear regression, the ridge, the lasso, the elastic net, the decision tree, the random forest (RF), the gradient boosting (GB), the extreme gradient boosting (XGBoost), the AdaBoost, and the k-nearest neighbor. The results reveal that MLP, GB, RF, and XGBoost show superior variance-explaining capabilities within the dataset. Their models can explain more than 86% of the variance in the dependent variable with a Mean Absolute Error of about 25.0, Mean Absolute Percentage Error of nearly or below 20%, Root Mean Square Error of less than 44.5, and R-squared more than 85%. This interesting finding might encourage civil engineers to focus on utilizing and testing these methods for practical shear strength estimate applications, while other regression algorithms might need dataset expansion and/or data engineering to augment future forecasting capabilities.

关键词： Machine learning SFRC Shear capacity Stirrup-free deep beams Regression boosting algorithm Neural network

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Assessing the Suitability of boosting Machine-Learning algorithms for Classifying Arsenic-Contaminated Waters: A Novel Model-Explainable Approach Using SHapley Additive exPlanations

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WATER 2022年第21期14卷 3509页

作者： Ibrahim, Bemah Ewusi, Anthony Ahenkorah, Isaac Univ Mines & Technol Dept Geol Engn POB 237 Tarkwa Ghana Univ South Australia UniSA STEM Mawson Lakes SA 5095 Australia

There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, "high arsenic" instead of "low arsenic", is as important as the prediction accuracy. In response, this study aims to explain model predictions by assessing the relationship between influencing input variables, i.e., pH, turbidity (Turb), total dissolved solids (TDS), and electrical conductivity (Cond), on arsenic mobility. The two main objectives of this study are to: (i) classify arsenic concentrations in multiple water sources using novel boosting algorithms such as natural gradient boosting (NGB), categorical boosting (CATB), and adaptive boosting (ADAB) and compare them with other existing representative boosting algorithms, and (ii) introduce a novel SHapley Additive exPlanation (SHAP) approach for interpreting the performance of ML models. The outcome of this study indicates that the newly introduced boosting algorithms produced efficient performances, which are comparable to the state-of-the-art boosting algorithms and a benchmark random forest model. Interestingly, the extreme gradient boosting (XGB) proved superior over the remaining models in terms of overall and single-class performance metrics measures. Global and local interpretation (using SHAP with XGB) revealed that high pH water is highly correlated with high arsenic water and vice versa. In general, high pH, high Cond, and high TDS were found to be the potential indicators of high arsenic water sources. Conversely, low pH, low Cond, and low TDS were the main indicators of low arsenic water sources. This study provides new insights into the use of ML and explainable methods for arsenic modelling.

关键词： arsenic modelling machine learning boosting algorithm explainability SHAP

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Extreme Gradient boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

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ENERGIES 2023年第24期16卷 7962页

作者： Zhao, Qinghe Liu, Xinyi Fang, Junlong Northeast Agr Univ Elect Engn & Informat Coll Harbin 150030 Peoples R China

Short-term power load forecasting refers to the use of load and weather information to forecast the Day-ahead load, which is very important for power dispatch and the establishment of the power spot market. In this manuscript, a comprehensive study on the frame of input data for electricity load forecasting is proposed based on the extreme gradient boosting algorithm. Periodicity was the first of the historical load data to be analyzed using discrete Fourier transform, autocorrelation function, and partial autocorrelation function to determine the key width of a sliding window for an optimization load feature. The mean absolute error (MAE) of the frame reached 52.04 using a boosting model with a 7-day width in the validation dataset. Second, the fusing of datetime variables and meteorological information factors was discussed in detail and determined how to best improve performance. The datetime variables were determined as a form of integer, sine-cosine pairs, and Boolean-type combinations, and the meteorological features were determined as a combination with 540 features from 15 sampled sites, which further decreased MAE to 44.32 in the validation dataset. Last, a training method for day-ahead forecasting was proposed to combine the Minkowski distance to determine the historical span. Under this framework, the performance has been significantly improved without any tuning for the boosting algorithm. The proposed method further decreased MAE to 37.84. Finally, the effectiveness of the proposed method is evaluated using a 200-day load dataset from the Estonian grid. The achieved MAE of 41.69 outperforms other baseline models, with MAE ranging from 65.03 to 104.05. This represents a significant improvement of 35.89% over the method currently employed by the European Network of Transmission System Operators for Electricity (ENTSO-E). The robustness of the proposal method can be also guaranteed with excellent performance in extreme weather and on special days.

关键词： STLF load forecast machine learning boosting algorithm

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Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas

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GEOCARTO INTERNATIONAL 2023年第1期38卷

作者： Xiong, Hanxiang Guo, Xu Wang, Yuzhou Xiong, Ruihan Gui, Xiaofan Hu, Xiaojing Li, Yonggang Qiu, Yang Tan, Jiayao Ma, Chuanming China Univ Geosci Sch Environm Studies Wuhan Peoples R China Shanghai Jiao Tong Univ Sch Environm Sci & Engn Shanghai Peoples R China Eastern Inst Technol Eastern Inst Adv Study Ningbo Peoples R China China Univ Geosci State Key Lab Biogeol & Environm Geol Wuhan Peoples R China Microsoft Res Asia Dept Machine Learning Beijing Peoples R China

This study makes a significant contribution to the field of groundwater potential mapping (GWPM) by exploring the application of ensemble learning models (ELMs), specifically boosting ensemble models (BEMs), which have not been fully utilized in GWPM. By employing six ELMs (random forest, AdaBoost, XGBoost, CatBoost, GBDT and LightGBM), along with Tree-structured Parzen Estimator in Luoning County, China, this study identifies key indicators (topographic position index, distance to rivers and topographic wetness index) and demonstrates the superior model performance of XGBoost compared to other ELMs. Additionally, correlation analysis confirms the accuracy of XGBoost in predicting relationships between important indicators and groundwater potentials. Finally, the findings provide valuable insights for sustainable groundwater management strategies in Luoning County and emphasize the need for further exploration of ELMs, development of comprehensive performance evaluation and indicator systems, reduction of the inconsistencies between indicators and predication results and practical research to support future sustainable groundwater management.

关键词： Groundwater potential mapping ensemble learning models boosting algorithm sustainable groundwater management

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AdaBoost Regression algorithm Based on Classification-Type Loss

AdaBoost Regression Algorithm Based on Classification-Type L...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Gao, Lin Kou, Peng Gao, Feng Guan, Xiaohong Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Provinc Peoples R China Xi An Jiao Tong Univ Syst Engn Inst SKLMS Xian Peoples R China

ISBN: (纸本)9781424467129

This paper presents a new concept of building classification-type loss for regression sample based on conversion between regression and classification problems used in Support Vector Regression (SVR). By introducing the classification-type loss to calculate example's error, AdaBoost algorithm can be generalized from classification to regression. A new boosting algorithm for regression, called ***.R which can be directly applied to a regression problem is proposed. SVR is used as its base learner. Its output is an ensemble of a team of regression functions. The employing of the classification-type loss makes the iterating process of ***.R act essentially on a converted binary classification problem. The output scheme of ***.R is also derived upon constructing decision function of the binary classification problem. Since it has the same application condition as AdaBoost, ***.R could satisfy the convergence proof of AdaBoost algorithm. The testing results for the considered data sets show that the new algorithm is effective.

关键词： boosting algorithm ensemble learning regression estimation support vector regression

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Automated Supervised Segmentation of Anatomical Fiber Tracts using an AdaBoost Framework

Automated Supervised Segmentation of Anatomical Fiber Tracts...

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IEEE International Conference on the Science of Electrical Engineering (ICSEE)

作者： Heker, Michal Amer, Rula Alexandroni, Guy Greenspan, Hayit Tel Aviv Univ Fac Engn Dept Biomed Engn Tel Aviv Israel Tel Aviv Univ Fac Engn Dept Elect Engn Tel Aviv Israel

ISBN: (纸本)9781509021529

Diffusion Magnetic Resonance Imaging (DMRI) can be used to reconstruct the main neural pathways in the brain, herein termed 'fibers'. Segmentation of anatomical tracts out of full brain fiber-sets acquired by tractography can lead to a better understanding of white matter diseases. Hence, we developed an automatic segmentation tool based on a renowned, supervised, machine-learning framework, called Viola-Jones [1]. We applied the algorithm for tract segmentation, using simple physical, statistical and geometrical features that can characterize the tract's fibers such as length, location, variance, FFT coefficients etc. An AdaBoost based learning framework was applied in order to select the most discriminative set of features for the classification of fibers to different anatomical tracts. Linear combinations of such features were used to construct classifiers. Those classifiers were arranged in a cascade which efficiently filters out fibers that do not belong to the desired tract. The algorithm was applied on a training set consisting of brains obtained from the Human Connectome Project. A cascade was learned for three different tracts. Performance evaluation was done by calculating the dice coefficients for each of the tested brains, yielding a result of around 90% for the tracts under evaluation, indicating a successful segmentation of the tracts of the entire brain.

关键词： white matter diffusion MRI tract segmentation classification boosting algorithm

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Improving Svm Learning Accuracy with Adaboost

Improving Svm Learning Accuracy with Adaboost

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4th International Conference on Natural Computation (ICNC 2008)

作者： Zhang, Xiaolong Ren, Fang Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9780769533049

Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm-Boost-SVM, which puts SVM into AdaBoost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples in the re-sampling process of AdaBoost, SVM appears to be more accurate. The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.

关键词： SVM boosting algorithm AdaBoost algorithm

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Diabetes Prediction Using Ensemble Perceptron algorithm 9

Diabetes Prediction Using Ensemble Perceptron Algorithm

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9th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Mirshahvalad, Roxana Zanjani, Nastaran Asadi Eastern Mediterranean Univ Dept Comp Engn Gazimagusa Cyprus Ecole Polytech Fed Lausanne Dept Elect Engn Lausanne Switzerland

ISBN: (纸本)9781509050017

Today, people's new way of life leads their eating habits towards fast-foods and ready-to-use products more than before. These foods contain large amounts of sugar and fat, which increase the number of people at risk of diabetes. Many people are required to get diabetes diagnosis by various blood tests regularly. These tests bring significant amounts of cost and take facilities and time when it comes to a large number of people. Machine learning algorithms can be used as computer aided systems to predict if a person is highly probable to have diabetes or not, in order to reduce huge number of people who require to take diagnosis blood tests, to save time and money. In this study, we proposed a learning algorithm which ensemble boosting algorithm with perceptron algorithm to improve performance of perceptron algorithm in prediction of undiagnosed patients. Proposed method is tested on three different publicly available datasets and compared with performance of perceptron algorithm. The results show that proposed algorithm outperform perceptron algorithm on average AUC basis.

关键词： ensemble learning perceptron algorithm machine learning algorithm boosting algorithm diabetes prediction

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