Improved Detection of Coronary Artery Disease Using DT-RFE Based Feature Selection and Ensemble Learning

Tyagi, Ashima; Singh, Vibhav Prakash; Gore, Manoj Madhava

doi:10.1007/978-3-030-96040-7_34

Cited by 5 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a wrapper algorithm, the RFE algorithm can consider the interaction and combination effects of features, which can lead to more accurate feature selection. 37,38 The RFE algorithm incorporates the model performance during the feature selection process, which ensures that the selected features are directly related to the model performance. In addition, the RFE algorithm is a flexible algorithm that can be used with various methods for model construction.…”

Section: Theoretical Backgroundsmentioning

confidence: 99%

Cross-domain decision method based on instance transfer and model transfer for fault diagnosis

Zhang,

Huang,

Liu

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

As the digitalization of industrial assets advances, data-driven fault diagnosis has increasingly garnered attention. However, models often underperform due to the lack of sufficient training data and the complexity of operational environments. In scenarios where a similar task with abundant data exists in the source domain, leveraging the knowledge embedded in this source data could be key to constructing an effective diagnostic model for the target domain. Following this idea, this study introduces a novel cross-domain decision method, weighted structure expansion and reduction (WSER), for fault diagnosis. This method initially extracts features from the time, frequency, and time-frequency domains. It then estimates data weights following the idea of instance transfer to mitigate the dissimilarity between the source and target data distributions. Based on these estimated weights, feature selection is further performed. The extracted source knowledge is subsequently transferred to the target domain using the proposed WSER method. The proposed method is applied on two public engineering fault datasets, and the results demonstrate the effectiveness of the proposed method in increasing the accuracy of fault diagnosis.

show abstract

Section: Theoretical Backgroundsmentioning

confidence: 99%