Multi-train: A semi-supervised heterogeneous ensemble classifier

Gu, Shengfeng; Jin, Yaochu

doi:10.1016/j.neucom.2017.03.063

Cited by 36 publications

(11 citation statements)

References 24 publications

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“…1. Heterogeneous ensembles that use both Input Input (1) Model (1) different types of models and different input features created by feature selection and feature extraction have shown better performance than those using single type of inputs [60], [61], partly due to the inherent better ability to create diversity [36]. Therefore, this work also employs different features and different types of models for promoting ensemble diversity without impairing accuracy, hoping to enhance the ability of the ensemble surrogate in uncertainty estimation.…”

Section: Heterogeneous Ensemble Generationmentioning

confidence: 99%

“…In machine learning, it has been theoretically proved that when a right balance between diversity and accuracy is met, an ensemble can provide more accurate predictions than any of its member alone [33]. Ensembles are often categorized into homogeneous ensembles (consisting of the same type of models typically generated by manipulating the data, like random sampling [34]) and heterogeneous ensembles (composed of different types of models [35] or different input features [36]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Guo

Jin

Ding

et al. 2019

IEEE Trans. Cybern.

Self Cite

177

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Gaussian processes (GPs) are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because GPs are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing GPs may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a GP and a homogeneous ensemble for infill sampling criteria in evolutionary multiobjective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with GPs and much more scalable in computational complexity to the increase in search dimension.

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Section: Heterogeneous Ensemble Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Guo

Jin

Ding

et al. 2019

IEEE Trans. Cybern.

Self Cite

177

View full text Add to dashboard Cite

show abstract

“…The performance of supervised classifiers has been explored in intrusion detection [29], robotics [18], semantic web [19], human posture recognition [30], face recognition [20], biomedical data classification [31], handwritten character recognition [22] and land cover classification [21]. Furthermore, an innovative semi-supervised heterogeneous ensemble classifier called Multi-train [32] was also proposed, where a justifiable comparison was made with other supervised classifiers, such as k-Nearest Neighbour (kNN), J48, Naïve Bayes, and random tree. Multi-train was also successfully achieved, and its prediction accuracy of unlabeled data was improved, which, therefore, can reduce the risk of incorrectly labeling the unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

et al. 2021

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Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

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“…In this section, we carry out comparisons of WEDA and other related methods: adaptive semi-unsupervised weighted oversampling (ASUWO) [53], the postprocessing technique for a support vector machine (BSVM) [54], a minimally spanned support vector machine (MSSVM) [55], a semi-supervised heterogeneous ensemble classifier (multi-train) [56], SSO-SMOTE-SSO [57], and vote-boosting ensembles (VBensembles) [58]. The main comparisons have three aspects: precision, recall rate, and execution time.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

WEDA: A Weak Emission-Line Detection Algorithm Based on the Weighted Ranking

et al. 2020

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The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with Hα emission line from massive data are necessary for the further study, while the most of methods existed currently do not adapt to such spectral data, especially for the spectra with weak Hα emission line. To address this issue, a new algorithm (named WEDA) for detection of spectra with Hα emission line is provided in this paper. Firstly, the difference factor µ between the line characteristics of the specific data is defined as its weight in recognizing of the whole lines table. Secondly, a tuning function f (τ, δ) based on the momentum formula is defined to update the weights during the process. In this step, the spectra with Hα emission line are analysed and classified as 3 different situations. The amount of spectra with Hα emission line is different in 3 different situations, so the speed of weight of update is different in 3 different situations. The weight of update helps us detect the data containing weak Hα emission line in the 3 situations. Based on this, a new integrated algorithm especially for the detection of the spectra with Hα is provided. In the end, by using several spectral datasets from the DR5 of LAMOST survey, experiments results indicate that the WEDA shows higher accuracy basically unaffected by the dataset size and the signal to noise ratio(SNR) than the other similar algorithms.

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Multi-train: A semi-supervised heterogeneous ensemble classifier

Cited by 36 publications

References 24 publications

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems

Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

WEDA: A Weak Emission-Line Detection Algorithm Based on the Weighted Ranking

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