The Effect of Data Pre-processing and Feature Selection on Learning

Dear Colleagues,

Recent advancements in machine learning and artificial intelligence have created a lot of research scope in both the theoretical and practical domains. Researchers of diverse fields are using learning algorithms in solving various identification, classification, and prediction problems. However, since we have a wide range of intelligent algorithms to choose from, selecting the best one for a given task can be tricky. Alternatives techniques are available for data pre-processing and feature selection as well. In this Special Issue, we aim to list the research works that explore the effects of various pre-processing and feature selection techniques on learning and the performance of different learning algorithms on a particular type of data.

The scope of this Special Issue includes but is not limited to:

1. The analysis of biomedical signals for classification and prediction tasks;
2. The analysis of biomedical images for classification, segmentation, and prediction tasks;
3. Novel application-oriented machine learning methods;
4. Mechanical, electrical, and structural fault signal analysis, detection, and classification;
5. Histopathological image analysis, classification, and segmentation;
6. Review articles on signal and image analysis;
7. Review articles on the machine learning algorithms used in a particular application;
8. Noise removal and synthetic data generation techniques;
9. Comparative discussion on various feature extraction, feature selection, and machine learning techniques;

Machine learning-based power system and smart grid applications.

Dr. Abdullah Al Nahid
Prof. Dr. Mehedi Masud
Dr. Anupam Kumar Bairagi
Dr. M A Parvez Mahmud
Guest Editors

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• machine learning
• signal processing
• image processing
• biomedical applications
• feature extraction
• feature selection
• deep learning
• comparative study