Unsupervised classification of noisy chromosomes.

Abstract

Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes. During the recognition phase, the problem is simply to calculate the string edit distance of the unknowns to the representatives chosen from the training phase and classify the unknowns accordingly. In this paper, a general method to tackle the difficult unsupervised induction problem is described. The success of the method is demonstrated by showing how the inductive agent learns weights in a dynamic manner that allows it to distinguish between noisy median and telocentric chromosomes without knowing their proper labels. The process of learning is characterized as the process of finding the right distance function, i.e. the distance function that can nicely separate the classes.