Classification of Post Office Outlets According to Risk of Incident

The Classification of Post Office Outlets According to Risk of Incident
Dr. Dragan Savic
University of Exeter
Exeter, Devon
England

Application Description

The task described above consisted of the use of data mining tools of all types to find patterns in a database supplied to us by the Royal Mail. The aim of the project was to evaluate novel and already existing ‘data mining’ tools. Data mining is the extraction of useful information from a database using artificial intelligence algorithms and neural networks were considered as a major part of this process. In many sections of the literature it is commented that neural networks often outperform their counterparts on predictive accuracy and achieving this was considered paramount in this project. The database consisted of a set of attributes about each Post Office outlet and an indication whether that office had been subject to an incident in the past three years. At the time of writing it has not been made clear to the investigator what constitutes such an incident. The task of each of the data mining tools was to discover some connection between the number of incidents and the attributes which are present in the database. If satisfactory accuracy can be achieved, then the classification algorithm (or a group of algorithms) should be able to be interrogated as to the classification of new Post Office outlets to ascertain whether they are at risk from an incident.

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