Computerized Pattern Recognition Applications to Chemical Analysis. Development of Interactive Feature Selection Methods for the K-Nearest Neighbor Technique.
Abstract
A systematic approach has been developed for feature selection in the application of the K-nearest neighbor (KNN) computerized pattern recognition method. The approach uses an operator-interactive computer system. A large number of potentially-useful features for classification of patterns can be screened for the most relevant members by a combination of recommended procedures. These include: (a) one-dimensional KNN classification of all patterns using each feature individually; (b) inspection of histogram displays of classification records for each feature; and (c) establishment of consensus classifications from combined one-dimensional results. A computerized trial-and-error procedure can then be implemented to find the best combination of a minimum number of features for accurate classification using the multi-dimensional KNN method. (Modified author abstract)
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 21, 1974
- Accession Number
- AD0775237
Entities
People
- Marty A. Pichler
- Sam P. Perone
Organizations
- Purdue University