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Discriminant analysis algorithm based on a distance function and on a Bayesian decision
A Villarroya1, M Ríos, J M Oller
1Departament d'Estadística, Universitat de Barcelona, Spain.
Biometrics
|September 1, 1995
Summary
A novel algorithm partitions sample spaces using distance functions for accurate individual population allocation. It provides confidence measures and computes error rates, outperforming existing discriminant analysis methods.
Area of Science:
- Statistics
- Machine Learning
- Population Genetics
Background:
- Accurate classification of individuals into distinct groups is crucial in various scientific fields.
- Existing discriminant analysis techniques have limitations in precision and confidence assessment.
Purpose of the Study:
- To introduce a new algorithm for individual allocation to populations.
- To provide a measure of allocation confidence and compute classification error rates.
Main Methods:
- The algorithm defines a finite partition of the sample space using a distance function.
- It employs a standard Bayesian decision rule for population allocation.
- Classification error rates are calculated using the leave-one-out method.
Main Results:
- The proposed algorithm demonstrates effective individual allocation to populations.
- It offers a confidence measure comparable to logistic regression.
- Performance was evaluated against Fisher's linear discriminant, quadratic discriminant, and logistic discrimination.
Conclusions:
- The new algorithm provides a robust method for population allocation with quantifiable confidence.
- It offers a competitive alternative to established discriminant analysis techniques.