Attributebyattribute

Attributebyattribute is a methodological approach in data analysis and decision making that emphasizes evaluating or processing objects by examining each attribute independently rather than aggregating across attributes. In practice, attribute-by-attribute analysis applies per-attribute normalization, scoring, or decision rules, and often preserves separate results for each attribute rather than collapsing them into a single composite score.

This approach is used in a variety of domains. In multi-criteria decision analysis (MCDA), it supports transparent, attribute-level reasoning that can be easier to explain to stakeholders. In data preprocessing for machine learning, per-attribute handling helps manage heterogeneous attribute scales and missing values. It is also employed in product benchmarking and sensor data analysis where explainability and traceability of conclusions are important.

Advantages of attribution-by-attribute methods include greater transparency and explainability, easier debugging and quality control at the attribute level, and robustness to differences in attribute scales. They also facilitate localized handling of missing data and outliers.

Limitations include the neglect of interactions and dependencies among attributes, which can overlook synergistic effects that a holistic view might reveal. When a final decision or score is required, combining attribute-level results can be challenging and may reintroduce subjectivity through weighting or rules. Effective use often depends on the specific problem context and whether interpretability is prioritized over capturing attribute interactions.

See also: multi-criteria decision analysis, feature-wise processing, per-attribute normalization, explainable AI.