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Learning Multi-Class Discriminative Patterns using Episode-Trees

Authors:
Eng-Jon Ong
Nicolas Pugeault
Andrew Gilbert
Richard Bowden

Keywords: Data mining, Classification, Episodes patterns, Decision 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.

Pages: 1 to 8

Copyright: Copyright (c) IARIA, 2016

Publication date: March 20, 2016

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-61208-465-7

Location: Rome, Italy

Dates: from March 20, 2016 to March 24, 2016