Download background

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

General circulation model wikipedia, lookup

Pattern recognition wikipedia, lookup

Machine learning wikipedia, lookup

Types of artificial neural networks wikipedia, lookup

Theoretical ecology wikipedia, lookup

Artificial neural network wikipedia, lookup

Numerical weather prediction wikipedia, lookup

Multiple-criteria decision analysis wikipedia, lookup

Predictive analytics wikipedia, lookup

Artificial intelligence wikipedia, lookup

History of numerical weather prediction wikipedia, lookup

Natural computing wikipedia, lookup

Transcript
BACKGROUND
Machine learning (ML) is the study of programs that improve their performance at
solving a task through experience. ML research has been conducted since the inception
of artificial intelligence in the 1950's. Today, one of the most common application areas
of ML is data mining (DM), or knowledge discovery. DM is the process of automatically
discovering hidden patterns in large amounts of data with the intention to use the
discovered patterns for explanation or prediction. The identified patterns are
represented by different types of models depending on which learning algorithm is
used.
PROBLEM. The objective of most DM tasks is to develop models for decision support.
However, the generated models (e.g.: neural networks, decision trees, conjunctive rules,
and so on) are often difficult to understand and quite often the knowledge provided
differs from that of the intended users (domain experts). As described in Pazzani (1997),
a physician is generally reluctant to use a ML-based decision support tool since he/she
cannot understand the decision process from looking at the model.
AIMS: Through-out the last 20 years, ML and DM technologies have been evaluated and
analyzed with a clear focus toward quantitative metrics like accuracy, training time,
prediction time, and memory usage. Qualitative criteria like usability, understandability,
comprehensibility, and interestingness have been touched upon or passed with a glance
but never deeply investigated. If DM models could be improved with respect to such
qualitative criteria, the intended users of DM-based decision support would certainly be
less reluctant to adopt to the new techniques, which in fact have proved to outperform
humans in terms of accurate predictions on many problems. The aim is to develop the
theoretical foundation and empirical evaluation methods for qualitative aspects of
models.
My talk will be focused on possibilities and issues with regard to these aims.
Dr. Niklas Lavesson
PhD coordinator
Blekinge Institute of Technology
[ email ] [email protected]
[ phone ] +46-(0)457-385675
[ homepage ] www.bth.se/com/nla