Merkmalbased

Merkmalbased is a term used in data analysis and machine learning to describe approaches that rely primarily on predefined attributes or features derived from raw data to perform inference or prediction. The term is rooted in the German word Merkmal, meaning feature or attribute, and is commonly used in European or multilingual technical contexts to emphasize a feature-centric methodology.

In a merkmalbased workflow, data are first subjected to feature extraction and, if needed, feature selection or engineering. The resulting feature set is then used by traditional machine learning models such as logistic regression, support vector machines, decision trees, or ensemble methods. This contrasts with end-to-end or deep learning approaches that learn representations directly from raw data.

Applications span computer vision, where features such as edges, textures, or descriptors like SIFT/HOG are used; speech processing with MFCCs, pitch, and spectral features; and natural language processing with TF-IDF, n-grams, or other handcrafted features. The merkmalbased paradigm is valued for interpretability, reproducibility, and lower data requirements, and it can be more transparent in regulated environments.

Disadvantages include potential performance gaps compared with modern deep learning on large-scale tasks, heavy reliance on domain expertise for feature engineering, and possible brittleness to domain shifts. It remains a practical baseline and is often used in hybrid systems that combine engineered features with learning.

See also feature engineering, end-to-end learning, machine learning.