Learning multi-class discriminative patterns using episode-trees

Abstract

In this paper, we aim to tackle the problem of recognising temporal sequences in the context of a multi-class problem. In the past, the representation of sequential patterns was used for modelling discriminative temporal patterns for different classes. Here, we have improved on this by using the more general representation of episodes, of which sequential patterns are a special case. We then propose a novel tree structure called a MultI-Class Episode Tree (MICE-Tree) that allows one to simultaneously model a set of different episodes in an efficient manner whilst providing labels for them. A set of MICE-Trees are then combined together into a MICE-Forest that is learnt in a Boosting framework. The result is a strong classifier that utilises episodes for performing classification of temporal sequences. We also provide experimental evidence showing that the MICE-Trees allow for a more compact and efficient model compared to sequential patterns. Additionally, we demonstrate the accuracy and robustness of the proposed method in the presence of different levels of noise and class labels.