Feature space and label space selection based on Error-correcting output codes for partial label learning

Lin, Guangyi; Xiao, Zuoxiang; Liu, Jia-Tong; Wang, Beizhan; Liu, Kun-Hong; Wu, Qingqiang

doi:10.1016/j.ins.2021.12.093

Cited by 10 publications

(3 citation statements)

References 34 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%

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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%

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:

View full text Add to dashboard Cite

show abstract

“…The transformation strategy includes binary learning, graph matching, and dictionary learning. In binary learning, Lin et al [29] used error-correcting output codes. They combined the feature space and label space for PLL.…”

Section: Introductionmentioning

confidence: 99%

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

Feng,

Guo,

Luo

et al. 2024

Electronics

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Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces the Partial Label Learning for Blood Cell Classification (P4BC) strategy, a method that trains neural networks using the blood cell attribute labeling data of weak annotations. Using morphological knowledge, we predefined candidate label sets for the blood cell attributes to blend this knowledge with deep learning. This improves the model’s prediction accuracy and interpretability in classifying attributes. This method effectively combines morphological knowledge with deep learning, an approach we refer to as knowledge alignment. It results in an 8.66% increase in attribute recognition accuracy and a 1.09% improvement in matching predictions to the candidate label sets, compared to the original method. These results confirm our method’s ability to grasp the characteristic information of blood cell attributes, enhancing the model interpretability and achieving knowledge alignment between hematological morphology and deep learning. Our algorithm ensures attribute classification accuracy and shows excellent cell category classification, highlighting its wide application potential and practical value in blood cell category classification.

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