Feature Extraction and Data Classification

Dear Colleagues,

Focus of this Special Issue

The focus of this Special Issue is on the critical role of feature engineering in the context of data mining. Feature engineering is a crucial step in the data mining pipeline, where raw data is transformed into meaningful features that enhance model performance, interpretability, and generalizability. This Special Issue aims to explore advanced methodologies, novel techniques, and innovative applications of feature engineering in data mining across various domains, including business analytics, healthcare, cybersecurity, and social networks.

Scope of this Special Issue

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

• Techniques for feature extraction: New methods for deriving useful features from raw data, especially in complex domains such as time series, image data, and unstructured data.
• Automated feature engineering: Methods such as autoML and deep learning-based feature engineering that can reduce manual intervention, making data mining tasks more efficient.
• Feature selection: Innovative approaches to selecting the most relevant features to improve model accuracy, prevent overfitting, and enhance interpretability.
• Domain-specific feature engineering: Tailored feature engineering techniques designed for specific industries such as healthcare (e.g., bioinformatics), finance (e.g., fraud detection), and environmental data mining.
• Feature transformation and normalization: The impact of various transformations on the performance of machine learning models, including scaling, discretization, and encoding.
• Evaluation metrics: How to assess the effectiveness of feature engineering techniques, including performance improvements and computational efficiency.

Purpose of this Special Issue

The primary purpose of this Special Issue is to:

• Highlight the importance of feature engineering in the data mining process, which is often an overlooked aspect despite its pivotal role in model success.
• Present cutting-edge research that advances the current understanding of feature engineering methods and how they can be adapted to modern data sets (e.g., big data, multi-modal data).
• Provide practical insights and guidelines for practitioners in data mining on how to design and select features that enhance the performance of machine learning models.

Promote interdisciplinary approaches: By showcasing research across multiple domains, this issue will encourage cross-domain fertilization of ideas and methods in feature engineering

Dr. Wanfu Gao
Dr. Jun Qin
Dr. Wentao Li
Guest Editors

Manuscript Submission Information

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• feature engineering
• feature selection
• multi-view
• multi-label