Vote-boosting ensembles

Sabzevari, Maryam; Martínez-Muñoz, Gonzalo; Suárez, Alberto

doi:10.1016/j.patcog.2018.05.022

Cited by 44 publications

(20 citation statements)

References 46 publications

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“…The standard classifiers for the heterogeneous ensemble considered in this study are described as follows: Bayes Theory (Naïve Bayes algorithm), Instance Learning (k Nearest Neighbour), Rule-based (RIPPER) and Tree methods (C4.5 Decision Tree). The voting combination method [14] [15] was adopted in this study for building the heterogeneous ensemble method. The voting method is a noncomplicated method of combining several predictions of varied or different models, and it can be implemented in a variety of approaches, including majority vote, minority vote and average of probabilities.…”

Section: A Datasetmentioning

confidence: 99%

Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study

Adeyemo¹,

Abdullah²,

Jhanjhi³

et al. 2019

IJACSA

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Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threats and vulnerabilities both internally and externally had led to the development of ceaseless research into Network Intrusion Detection System (NIDS). Therefore, an empirical study was conducted on the effectiveness of deep learning and ensemble methods in NIDS, thereby contributing to knowledge by developing a NIDS through the implementation of machine and deep-learning algorithms in various forms on recent network datasets that contains more recent attacks types and attackers' behaviours (UNSW-NB15 dataset). This research involves the implementation of a deep-learning algorithm-Long Short-Term Memory (LSTM)-and two ensemble methods (a homogeneous method-using optimised bagged Random-Forest algorithm, and a heterogeneous method-an Averaged Probability method of Voting ensemble). The heterogeneous ensemble was based on four (4) standard classifiers with different computational characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree). The respective model implementations were applied on the UNSW_NB15 datasets in two forms: as a two-classed attack dataset and as a multi-attack dataset. LSTM achieved a detection accuracy rate of 80% on the two-classed attack dataset and 72% detection accuracy rate on the multi-attack dataset. The homogeneous method had an accuracy rate of 98% and 87.4% on the two-class attack dataset and the multi-attack dataset, respectively. Moreover, the heterogeneous model had 97% and 85.23% detection accuracy rate on the two-class attack dataset and the multi-attack dataset, respectively.

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Section: A Datasetmentioning

confidence: 99%

Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study

Adeyemo¹,

Abdullah²,

Jhanjhi³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…In this study, vote-boosting method is used as the aggregation rule which specifies the instance weights based on the degree of the agreement or disagreement among its assigned labels by the base classifiers in the last ensemble layer. Previous studies have shown the robustness to class-label noise ability for vote-boosting method [24].…”

Section: Feature Rankingmentioning

confidence: 97%

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

Khatibi

Hanifi

Sepehri

et al. 2021

BMC Pregnancy Childbirth

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Background Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. Method A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features. Results IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90%, sensitivity of 91%, specificity of 88%. The discrimination of the proposed SE is assessed and the average AUC of ±95%, CI of 90.51% ±1.08 and 90% ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE. Conclusions Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.

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“…Boosting is one of the well known forms of this paradigm, AdaBoost algorithms in particular. Also, several sequential ensemble approaches have been recently proposed in the literature such as Voteboosting algorithm [29], SENF approach [30] and SEL framework [31]. On the other hand, the parallel ensemble paradigm, which is more popular and easier to implement, draws on the independence and diversity between the base learners since combining their independent decisions can reduce the classification error effectively [32].…”

Section: B Ensemble Rule-base Systemsmentioning

confidence: 99%

ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification

2021

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The learning of classification models to predict class labels of new and previously unseen data instances is one of the most essential tasks in data mining. A popular approach to classification is ensemble learning, where a combination of several diverse and independent classification models is used to predict class labels. Ensemble models are important as they tend to improve the average classification accuracy over any member of the ensemble. However, classification models are also often required to be explainable to reduce the risk of irreversible wrong classification. Explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc. Unfortunately, ensemble learning decreases the level of explainability of the classification, as the analyst would have to examine many decision models to gain insights about the causality of the prediction. The aim of the research presented in this paper is to create an ensemble method that is explainable in the sense that it presents the human analyst with a conditioned view of the most relevant model aspects involved in the prediction. To achieve this aim the authors developed a rule-based explainable ensemble classifier termed Ranked ensemble G-Rules (ReG-Rules) which gives the analyst an extract of the most relevant classification rules for each individual prediction. During the evaluation process ReG-Rules was evaluated in terms of its theoretical computational complexity, empirically on benchmark datasets and qualitatively with respect to the complexity and readability of the induced rule sets. The results show that ReG-Rules scales linearly, delivers a high accuracy and at the same time delivers a compact and manageable set of rules describing the predictions made.

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Vote-boosting ensembles

Cited by 44 publications

References 46 publications

Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study

Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification

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