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On the combination of two visual cognition systems using combinatorial fusion
Amy Batallones1, Kilby Sanchez2, Brian Mott1
1Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
Brain Informatics
|October 18, 2016
Summary
Combining two visual cognition systems using weighted averages improves performance only when both systems are accurate and diverse. This research clarifies when and why combining systems enhances decision-making in visual cognition.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Machine Learning
Background:
- Statistical methods like simple and weighted averages are used to combine decisions from multiple visual cognition systems.
- Existing methods can improve individual system performance, but the conditions for this improvement are not well understood.
Purpose of the Study:
- To investigate the conditions under which combining visual cognition systems leads to improved performance.
- To understand the role of system performance and diversity in successful combination strategies.
Main Methods:
- Transforming visual cognition systems into scoring systems based on statistical means (M1, M2, M3).
- Utilizing combinatorial fusion to analyze combined system results.
- Examining performance and diversity metrics on a dataset from a twelve-trial experiment.
Main Results:
- Combination using weighted means (M2, M3) enhances individual systems only if both systems exhibit good performance.
- Diversity between the two systems is a critical factor for successful performance improvement through combination.
- The study provides specific criteria for when system combination is beneficial.
Conclusions:
- The effectiveness of combining visual cognition systems depends on the performance and diversity of the individual systems.
- Weighted averaging methods are most effective when applied to systems that are both accurate and diverse.
- This research offers a framework for understanding and optimizing the fusion of multiple cognitive systems.
Keywords:
Cognitive diversityCombinatorial fusion analysis (CFA)Decision-makingRank-score characteristics (RSC) functionVisual cognitionMore Related Videos
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