Members of the LUCID group are set to give a tutorial on
Using intervals to capture and handle uncertainty
at the World Congress on Computational intelligence (WCCI), July 19-24, 2020, Glasgow, UK
Uncertainty is pervasive across data and data sources, from sensors in engineering applications to human preference and expertise in areas as diverse as marketing to cyber security. Appropriate handling of such uncertainties depends upon three main stages: capture, modelling, and analysis of/reasoning with results.
In recent years, interest has surged in using data types that are fundamentally uncertain – in particular intervals (rather than exact numbers). This has promoted novel research into multiple facets of handling uncertainty using interval-values. This includes capturing the uncertainty at source, and modelling it using intervals or higher-level models such as fuzzy sets. A variety of approaches to analysing said data have been introduced, from interval arithmetic and statistics on intervals, to similarity and distance measures applied to both ‘raw’ interval-valued datasets and fuzzy set models of the original data.
Going forward, it is expected that the use of intervals within machine learning and AI techniques will continue to grow, providing an intuitive means of capturing, accounting for, and communicating uncertainty in data.
This tutorial is designed to give researchers a practical introduction to the use of intervals for handling uncertainty in data. The tutorial will discuss relevant types and sources of uncertainty before proceeding to review and demonstrate practical approaches and tools that enable the capture, modelling and analysis of interval-valued data. This session will provide participants with an end-to-end overview and in-depth starting point for leveraging intervals within their own research.
The tutorial is structured into four main components:
1. Capturing intervals from people
The first part of the tutorial will discuss the challenges behind capturing intervals in practice, before providing some practical solutions. This will include the underlying rationale, the nature and different types of intervals – and why these matter. As a use-case, we will discuss the elicitation of intervals within the quantitative social sciences, as part of a recently introduced interval-valued questionnaire approach using a freely available software platform: DECSYS.
2. Handling and analysing interval-valued data
The second part of the tutorial will review key techniques for handling ‘raw’ interval-valued data, including interval arithmetic and the computation of summary statistics – along with associated challenges (e.g. the dependency problem).
3. Modelling intervals using fuzzy sets
Beyond handling interval-valued data directly, a variety of approaches have been developed to model multi-source interval-valued data using fuzzy sets. We will discuss and demonstrate key algorithms, focussing in particular on the Interval Agreement Approach (IAA), which is designed to model interval-valued datasets while minimising modelling assumptions (e.g. outlier removal).
4. Case studies
In the final part of the tutorial, we will discuss a set of recent studies. These serve as real-world examples – demonstrating the efficacy of intervals through research and in applications that range from cyber security to engineering and psychology.
The overall time of the tutorial will be three hours, with approximately 40 minutes per section, and a 20 minute break.
There are no pre-requisites for this tutorial, although a familiarity with fuzzy sets will be an advantage.
Prof. Christian Wagner, Prof. Vladik Kreinovich, Dr Josie McCulloch, Dr Zack Ellerby
The organisers have a track record of organising and chairing special sessions at previous IEEE conferences, annually, since 2009.
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