Dr Gavin Smith's Outputs (10)

Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data (2022)
Presentation / Conference Contribution
Mansilla, R., Smith, G., Smith, A., & Goulding, J. (2022, December). Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data. Presented at 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application t... Read More about Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data.

Model Class Reliance for Random Forests (2020)
Presentation / Conference Contribution
Smith, G., Mansilla Lobos, R., & Goulding, J. (2020, December). Model Class Reliance for Random Forests. Presented at 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada

Variable Importance (VI) has traditionally been cast as the process of estimating each variable's contribution to a predictive model's overall performance. Analysis of a single model instance, however, guarantees no insight into a variables relevance... Read More about Model Class Reliance for Random Forests.

FIMS: Identifying, Predicting and Visualising Food Insecurity (2020)
Presentation / Conference Contribution
Lucas, B., Smith, A., Smith, G., Perrat, B., Nica-Avram, G., Harvey, J., & Goulding, J. (2020, April). FIMS: Identifying, Predicting and Visualising Food Insecurity. Presented at The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, Taipei Taiwan

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely... Read More about FIMS: Identifying, Predicting and Visualising Food Insecurity.

The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions (2018)
Presentation / Conference Contribution
Engelmann, G., Smith, G., & Goulding, J. (2018, December). The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions. Presented at 2018 IEEE international Conference on Big Data, Seattle, USA

Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio... Read More about The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions.

Event series prediction via non-homogeneous Poisson process modelling (2016)
Presentation / Conference Contribution
Goulding, J., Preston, S. P., & Smith, G. (2016, December). Event series prediction via non-homogeneous Poisson process modelling. Presented at 2016 IEEE International Conference on Data Mining (ICDM), Barcelona, Spain

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature... Read More about Event series prediction via non-homogeneous Poisson process modelling.

A novel symbolization technique for time-series outlier detection (2015)
Presentation / Conference Contribution
Smith, G., & Goulding, J. A novel symbolization technique for time-series outlier detection. Presented at 2015 IEEE International Conference on Big Data

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where sp... Read More about A novel symbolization technique for time-series outlier detection.

AMP: a new time-frequency feature extraction method for intermittent time-series data (2015)
Presentation / Conference Contribution
Barrack, D. S., Goulding, J., Hopcraft, K., Preston, S., & Smith, G. AMP: a new time-frequency feature extraction method for intermittent time-series data. Presented at 1st International Workshop on Mining and Learning from Time Series (MiLeTS)

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques... Read More about AMP: a new time-frequency feature extraction method for intermittent time-series data.

The potential of electromyography to aid personal navigation (2014)
Presentation / Conference Contribution
Pinchin, J., Smith, G., Hill, C., Moore, T., & Loram, I. The potential of electromyography to aid personal navigation. Presented at 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014)

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg mi... Read More about The potential of electromyography to aid personal navigation.

A refined limit on the predictability of human mobility (2014)
Presentation / Conference Contribution
Smith, G., Wieser, R., Goulding, J., & Barrack, D. A refined limit on the predictability of human mobility. Presented at 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiot... Read More about A refined limit on the predictability of human mobility.

Towards optimal symbolization for time series comparisons (2013)
Presentation / Conference Contribution
Smith, G., Goulding, J., & Barrack, D. Towards optimal symbolization for time series comparisons. Presented at IEEE 13th International Conference on Data Mining Workshops (ICDMW 2013)

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the... Read More about Towards optimal symbolization for time series comparisons.