A novel ensemble method for k-nearest neighbor

Zhang, Youqiang; Cao, Guo; Wang, Bisheng; Li, Xuesong

doi:10.1016/j.patcog.2018.08.003

Cited by 108 publications

(45 citation statements)

References 38 publications

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“…In the second ensemble system, an ensemble of RSCs is constructed on the random subset of attributes obtained from the original attribute set. In [9], Zhang et al proposed a new ensemble system by training…”

Section: Related Work 21 Ensemble Methodsmentioning

confidence: 99%

“…where is the indicator function which returns 1 if the argument is true and 0 otherwise. The empirical 0-1 loss over the entire training observations is given by (9) and the value of is obtained by minimizing subjected to…”

Section: The Proposed Model For Ensemble Learningmentioning

confidence: 99%

“…Classical optimization methods may be more efficient than EA when solving linear or strongly convex problems, for non-differentiable, discontinuous, or multi-modal objective functions that appear in many real-life applications, EA can be a better choice. In this study, we use the Artificial Bee Colony (ABC) algorithm, a popular EA, to search for the optimal solution of the optimization problem in (9). The ABC algorithm, proposed by Karaboga [30], is a member of the swarm intelligence family, a meta-heuristic search algorithm inspired by the intelligent foraging behavior of honey bee swarm.…”

Section: The Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble Selection based on Classifier Prediction Confidence

Nguyen

Luong

Dang

et al. 2020

Pattern Recognition

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Related Work 21 Ensemble Methodsmentioning

confidence: 99%

Section: The Proposed Model For Ensemble Learningmentioning

confidence: 99%

Section: The Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Selection based on Classifier Prediction Confidence

Nguyen

Luong

Dang

et al. 2020

Pattern Recognition

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“…Least absolute shrinkage and selection operator (LASSO) performs variable selection and regularization while fitting a generalized linear model [37]. KNeighbors regression (KNR) [38] determines the regression values of the test samples by the values of the surrounding K training samples.…”

Section: Methodsmentioning

confidence: 99%

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Zhang

Chen

et al. 2020

IEEE Access

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Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems. Most data-based methods are limited to a single model, which is susceptible to various factors like environmental variability and diversity of operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners to mitigate the impact of multi-operating conditions. The selection of meta-learners follows a multi-objective evolutionary algorithm named non-dominated sorting genetic algorithms-II to balance the two conflicting objectives in terms of accuracy and diversity. Then the eventually evolved meta-learners are integrated by the meta-classifier for RUL estimation. In addition, a long-short-term feature extraction strategy is proposed to capture more degradation information from lifecycle data dynamically. Extensive experiments are performed on aero-engine dataset and battery dataset provided by NASA, which achieves the higher prognostic accuracy compared with the single models and existing methods. INDEX TERMS Remaining useful life (RUL), reliability, optimal stacking ensemble, meta-learners.

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“…Next computed features were classified. To classify features the NN classifier [29,30,31] was used (please see Section 2.3). There are 145 features in the feature vector.…”

Section: Developed Acoustic Based Approachmentioning

confidence: 99%

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Głowacz

2019

Sensors

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Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TED = 96%, TECG-A = 97%, TECG-B = 100%).

show abstract

A novel ensemble method for k-nearest neighbor

Cited by 108 publications

References 38 publications

Ensemble Selection based on Classifier Prediction Confidence

Ensemble Selection based on Classifier Prediction Confidence

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

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