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X-ORIGINAL-URL:https://optima.org.au/
X-WR-CALNAME:OPTIMA
X-WR-CALDESC:ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications
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UID:MEC-a07c2f3b3b907aaf8436a26c6d77f0a2@optima.org.au
DTSTART:20220504T060000Z
DTEND:20220504T070000Z
DTSTAMP:20220426T012500Z
CREATED:20220426
LAST-MODIFIED:20220505
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:OPTIMA Seminar Series 04 May 2022
DESCRIPTION:Speaker: Dr Sevvandi Kandanaarachchi -RMIT\nEvaluating algorithm portfolios using Item Response Theory\nHow do you evaluate a portfolio of algorithms? Suppose we have the results for a set of algorithms on a given set of problems. We can find which algorithm performs best for each problem and find the algorithm that performs best on the greatest number of problems. But, there is a limitation to this approach. We are only looking at the overall best! Suppose a certain algorithm gives the best performance on hard problems, but not on easy problems. We would miss this algorithm by using the “overall best” approach.\nItem Response Theory (IRT) is used to design, analyse and score test questions/questionnaires that measure hidden qualities such as stress proneness, political inclinations, or verbal/mathematical ability. It is a methodology used in educational psychometrics. Participants take tests and IRT is used to determine the ability of participants and discrimination and difficulty of test questions. We use a novel mapping of the traditional IRT framework modified to the algorithm evaluation domain. Using this new mapping, we elicit a richer suite of characteristics including stability and anomalousness that describe important aspects of algorithm performance. We find the strengths and weaknesses of algorithms in the problem space. Using the algorithm strengths and weaknesses we construct a smaller portfolio of algorithms that gives a good performance.\nBio: Sevvandi is an applied mathematician working on data science/analytics related topics such as anomaly detection, meta-learning and streaming data. She likes working on real-world problems, especially ones that are motivated by industry. She came to machine/statistical learning from a pure mathematics background.\nWED 04 MAY 4PM – 5PM AEST\nZOOM MEETING ID: 873 1557 5255; PASSWORD: 778635\n
URL:https://optima.org.au/events/optima-seminar-series-04-may-2022/
CATEGORIES:Events
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