A New Feature Selection Method and Comparison of Classification Performances of Feature Selection Methods

Erbayram, Tenzile; Erişoğlu, Murat

doi:10.51541/nicel.909876

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It primarily focuses on removing uninformative or unnecessary predictive elements from the model. 39 The number of features needs to be reduced both to simplify the calculation of the model and, in some cases, to improve model performance. In this study, the ReliefF (RF) algorithm and the principal components analysis were preferred as shown in the symbolic representation in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

Fault diagnosis for overcharge and undercharge conditions in refrigeration systems using infrared thermal images

Katircioğlu

Cingiz

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

With the increase in industrialization and the number of people and buildings, the need for refrigeration systems has also increased. Maintenance of these systems, malfunctions and their late detection cause time and cost problems. Therefore, in this study, a machine learning application is recommended to diagnose the refrigerant undercharge and refrigerant overcharge faults that may occur in the refrigeration system by using infrared images. Firstly, infrared images obtained from normal charge, undercharge and overcharge situations in the refrigeration system, are passed through the two-dimensional discrete wavelet transform (2D-DWT) and the images are decomposed. Then, statistical texture features from the original input images are obtained by separating infrared images. The dimensionality of the extracted features is reduced using the principal component analysis (PCA) and the ReliefF (RF) algorithm. Finally, these selected features are applied to the K nearest neighbor (k-NN), naive Bayes algorithm (NBA), decision tree (DT), and cascade forward neural network (CFNN) classifiers. It has been found that RF-based feature selection is useful in obtaining the optimal feature vector. The classification results revealed that CFNN outperforms k-NN, NBA, and DT. Compared to traditional electrical measurements and fault detection methods, it has been shown that the recommended system is feasible due to its features such as ease of use, remote measurement, and self-adaptive recognition.

show abstract

Section: Methodsmentioning

confidence: 99%