An approach to boosting from positive-only data

Ensemble techniques have recently been used to enhance the performance of machine learning methods.However, current ensemble techniques for classification require both positive and negative data to produce a result that is both meaningful and useful.Negative data is, however, sometimes difficult, expensive or impossible to access.In this thesis a learning framework is described that has a very close relationship to boosting. Within this framework a methodis described which bears remarkable similarities to boosting stumps and thatdoes not rely on negative examples.This is surprising since learning from positive-only data has traditionally been difficult. An empirical methodology is described and deployed for testing positive-only learning systems using commonlyavailable multiclass datasets to compare these learning systems with each other and with multiclass learning systems.Empirical results show that our positive-only boosting-like method learns, using stumps as a base learner and from positive data only, successfully, and in theprocessdoes not pay too heavy a price in accuracy compared to learners that have access to both positive and negative data.We also describe methods of using positive-only learners on multiclass learning tasks and vice versa and empirically demonstrate the superiority of our method of learning in a boosting-like fashion from positive-only data over a traditional multiclass learner converted to learn from positive-only data.Finally we examine some alternative frameworks, such as when additional unlabelled training examples are given.Some theoretical justifications of the results and methods are also provided