The design of error-correcting output codes algorithm for the open-set recognition

Liu, Kun-Hong; Zhan, Wang-Ping; Yi-Fan, Liang; Zhang, Yanan; Guo, Hong-Zhou; Yao, Junfeng; Wu, Qingqiang; Hong, Qingqi

doi:10.1007/s10489-021-02854-w

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…For this, it uses a twostage strategy, namely encoding and decoding, which are described as follows. 27,36 Encoding stage. In this stage, a codeword is constructed for each class in the multiclass classification problem.…”

Section: Distance Class Features Distance Class Features Indicate The...mentioning

confidence: 99%

“…For this, it uses a two-stage strategy, namely encoding and decoding, which are described as follows. 27,36…”

Section: Technical Backgroundmentioning

confidence: 99%

“…The ECOC framework has been introduced by Dietterich and Bakiri and originates from information and communication engineering fields. 26,27 It breaks down a multiclass classification problem into multiple binary classification problems and possesses good ability to correct the classifier bias and variance errors. 28 The ECOC strategy has been effectively utilized in a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

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Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Rai,

Kapu,

Balaji

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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Gearbox failures have a detrimental effect on the machine performance that affects the production capacity and economic benefits of a manufacturing industry in an adverse manner. Early detection of faults in gears helps to prevent sudden machine failures. Previously, many signal processing and artificial intelligence techniques have been successfully applied to diagnose gear faults by analyzing the machine vibrations. These techniques have repeatedly emphasized on the need to extract quality features from gear vibration signals and using artificial intelligence techniques that can identify multiple types of faults simultaneously. As such, in this article, new features called as visibility graph features based on complex network theory have been proposed to extract fault information from vibration signals. In addition, a new artificial intelligence framework for multifault classification known as error-correcting output codes-multiclass support vector machine is proposed to detect gear faults. To the best of authors’ knowledge, these features along with the error-correcting output codes-artificial intelligence framework have been rarely utilized for fault diagnosis purpose. The proposed approach has been applied on experimental gear vibration data to validate its effectiveness. First, the vibration signals are transformed to graphs, and various graph properties are computed to serve as fault features. Then, the fault features are supplied to train the error-correcting output codes-multiclass support vector machine model and learn the fault patterns. Finally, the gear fault classification accuracies are determined for various gear test conditions and compared with those of existing methods. It is observed that the proposed approach provides an average improvement of 4.22%, 9.93% and 11.8% over the time-domain features, time–frequency domain features and voting-based multiclass support vector machine classifier, respectively, for different operating conditions. Furthermore, the suggested technique augments the classification accuracies by 10%, 7.5% and 6.7%, respectively, as compared with the deep-learning models, namely standard stacked sparse autoencoder, deep neural network and convolution neural network.

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Section: Distance Class Features Distance Class Features Indicate The...mentioning

confidence: 99%

“…For this, it uses a two-stage strategy, namely encoding and decoding, which are described as follows. 27,36…”

Section: Technical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Rai,

Kapu,

Balaji

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

The design of error-correcting output codes based deep forest for the micro-expression recognition

Lin

Liong

et al. 2022

Appl Intell

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An adaptive error-correcting output codes algorithm based on gene expression programming and similarity measurement matrix

Xie,

He,

Pan

et al. 2024

Pattern Recognition

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The design of error-correcting output codes algorithm for the open-set recognition

Cited by 5 publications

References 54 publications

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

The design of error-correcting output codes based deep forest for the micro-expression recognition

An adaptive error-correcting output codes algorithm based on gene expression programming and similarity measurement matrix

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