Feature selection and learning curves of a multilayer perceptron chromosome classifier
Boaz Lerner, Hugo Guterman, Its'Hak Dinstein, Yitzhak Romem · 2002
A multilayer perceptron (MLP) neural network (NN) was used for human chromosome classification. The significance of relevant chromosome features to the classification procedure was evaluated using a feature selection mechanism. It yielded the benefit of using only a part of the available features to get performance close to the ultimate one, classifying chromosomes of 5 types. Only 10-20 examples were required for the MLP NN classifier to reach its supreme performance disregarding the number of features used. Furthermore, the empirical entropic error of the classifier was found to be highly comparable to the 1/t function that is a universal learning curve.