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 and to analyse the behaviour of the learning system when using different ratios of labelled to unlabelled data. In this paper various methods for learning from labelled and unlabelled data are first discussed and categorised into one of three major groups: pre-labelling, post-labelling and semi-supervised approaches. Their generalised formal description and extensive experimental analysis is then provided. 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 based on random selection of labelled samples show high variability and the performance of the final classifier is more dependent on how reliable the labelled data samples are rather than use of additional unlabelled data. In response to this finding three types of static (one-step) selection methods guided by a clustering information and various options of allocating a number of samples within clusters and their distributions have been proposed and analysed. A significant improvement compared to the random selection of the labelled samples have been observed when using these selective sampling techniques.
['Bogdan Gabrys', 'Lina Petrakieva']
Combining labelled and unlabelled data in the design of pattern classification systems