research
Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems
- Publication date
- Publisher
Abstract
There has been much interest in applying techniques that incorporate knowledge from unlabelled data
into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on
real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data.
In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised
approaches using different ratios of labelled to unlabelled samples is presented. The experimental results
show that when supported by unlabelled samples much less labelled data is generally required to build a classifier
without compromising the classification performance. If only a very limited amount of labelled data is available the
results show high variability and the performance of the final classifier is more dependant on how reliable the labelled
data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and
unlabelled data have been shown to offer most significant improvements when natural clusters are present in the
considered problem