Identification with iterative nearest neighbors using domain knowledge

A new iterative and interactive algorithm called CSN (Classification by Successive Neighborhood) to be used in a complex descriptive objects identification approach is presented. Complex objects are those designed by experts within a knowledge base to describe taxa (monography species) and also real organisms (collection specimens). The algorithm consists of neighborhoods computations from an incremental basis of characters using a dissimilarity function which takes into account structures and values of the objects. A discriminant power function is combined with domain knowledge on the features set at each iteration. It is shown that CSN consistently outperforms methods such as identification trees and simplifies interactive classification processes comparatively to search for K-Nearest-Neighbors method. Index Terms — identification, Similarity, K-Nearest-Neighbors, Decision Trees, structured data, knowledge base, life science. —————————— u ——————————