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Learning multi-class discriminative patterns using episode-trees

Ong, Eng-Jon, Pugeault, Nicolas, Gilbert, Andrew and Bowden, Richard (2016) Learning multi-class discriminative patterns using episode-trees In: Cloud Computing 2016 - The Seventh International Conference on Cloud Computing, GRIDs, and Virtualization, 2016-03-20 - 2016-03-24, Rome, Italy.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 20 March 2016
Copyright Disclaimer : Copyright © (2016) by International Academy, Research, and Industry Association (IARIA)
Uncontrolled Keywords : Data Mining, Classification, Episodes Patterns, Decision Trees
Related URLs :
Additional Information : Manuscript presented at the IARIA PATTERNS 2016 conference, 20-24th of March in Rome. (c) IARIA 2016.
Depositing User : Symplectic Elements
Date Deposited : 26 Oct 2016 11:19
Last Modified : 16 Jan 2019 17:09

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