Learning in mobile context-aware applications

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Title: Learning in mobile context-aware applications
Author(s): Smith, Jeremiah
Item Type: Thesis or dissertation
Abstract: This thesis explores and proposes solutions to the challenges in deploying context-aware systems that make decisions or take actions based on the predictions of a machine learner over long periods of time. In particular, this work focuses on mobile context-aware applications which are intrinsically personal, requiring a specific solution for each individual that takes into account user preferences and changes in user behaviour as time passes. While there is an abundance of research on mobile context-aware applications which employ machine learning, most does not address the three core challenges required to be deployable over indefinite periods of time. Namely, (1) user-friendly and longitudinal collection and labelling of data, (2) measuring a user’s experienced performance and (3) adaptation to changes in a user’s behaviour, also known as concept drift. This thesis addresses these challenges by introducing (1) an infer-and-confirm data collection strategy which passively collects data and infers data labels using the user’s natural response to target events, (2) a weighted accuracy measure Aw as the objective function for underlying machine learners in mobile context-aware applications and (3) two training instance selection algorithms, Training Grid and Training Clusters which only forget data points in areas of the data space where newer evidence is available, moving away from the traditional time window based techniques. We also propose a new way of measuring concept drift indicating which type of concept drift adaption strategy is likely to be beneficial for any given dataset. This thesis also shows the extent to which the requirements posed by the use of machine learning in deployable mobile context-aware applications influences its overall design by evaluating a mobile context-aware application prototype called RingLearn, which was developed to mitigate disruptive incoming calls. Finally, we benchmark our training instance selection algorithms over 8 data corpuses including the RingLearn corpus collected over 16 weeks and the Device Analyzer corpus which logs several years of smartphone usage for a large set of users. Results show that our algorithms perform at least as well as state-of-the-art solutions and many times significantly better with performance delta ranging from -0.2% to +11.3% compared to the best existing solutions over our experiments.
Content Version: Open Access
Publication Date: Aug-2015
Date Awarded: Feb-2016
Advisor: Dulay, Naranker
Sponsor/Funder: European Commission
Funder's Grant Number: 264738
Department: Computing
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Computing PhD theses

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