A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis

• Published in: Signal, image and video processing image and video processing, 2024-07, Vol.18 (5), p.4589-4603
• Main Authors: Şenol, Ali, Talan, Tarık, Aktürk, Cemal
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Computer Imaging; Computer Science; Datasets; Diagnosis; Image Processing and Computer Vision; Machine learning; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Performance evaluation; Projection model; Quality assessment; Signal,Image and Speech Processing; Sleep disorders; Vision
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-024-03097-1

In the machine learning area, having a large number of irrelevant or less relevant features to the result of the dataset can reduce classification success and run-time performance. For this reason, feature selection or reduction methods are widely used. The aim is to eliminate irrelevant features or transform the features into new features that have fewer numbers and are relevant to the results. However, in some cases, feature reduction methods are not sufficient on their own to increase success. In this study, we propose a new hybrid feature projection model to increase the classification performance of classifiers. For this goal, the MCMSTClustering algorithm is used in the data preprocessing stage of classification with various feature projection methods, which are PCA, LDA, SVD, t-SNE, NCA, Isomap, and PR, to increase the classification performance of the sleep disorder diagnosis. To determine the best parameters of the MCMSTClustering algorithm, we used the VIASCKDE Index, Dunn Index, Silhouette Index, Adjusted Rand Index, and Accuracy as cluster quality evaluation methods. To evaluate the performance of the proposed model, we first appended class labels produced by the MCMSTClustering to the dataset as a new feature. We applied selected feature projection methods to decrease the number of features. Then, we performed the kNN algorithm on the dataset. Finally, we compared the obtained results. To reveal the efficiency of the proposed model, we tested it on a sleep disorder diagnosis dataset and compared it with two models that were pure kNN and kNN with the feature projection methods used in the proposed approach. According to the experimental results, the proposed method, in which the feature projection method was Kernel PCA, was the best model with a classification accuracy of 0.9627. In addition, the MCMSTClustering algorithm increases the performance of PCA, Kernel PCA, SVD, t-SNE, and PR. However, the performance of the LDA, NCA, and Isomap remains the same.