Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality
2009 (English)Licentiate thesis, monograph (Other academic)
Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect
more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression,to complex powerful ones like artificial neural networks. Complex
models usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns.
The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for a
specific technique, since these settings also are problem dependent.
One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction.
In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems.
In these cases, predictions need to be manually analyzed and adjusted.
Here, opaque models like ensembles have a disadvantage, since the
analysis requires understandable models. To overcome this deficiency
of opaque models, researchers have developed rule extraction
techniques that try to extract comprehensible rules from opaque
models, while retaining sufficient accuracy.
This thesis suggests a straightforward but comprehensive method for
predictive modeling in situations with poor data quality. First,
ensembles are used for the actual modeling, since they are powerful,
robust and require few design choices. Next, ensemble uncertainty
estimations pinpoint predictions that need special attention from a
decision maker. Finally, rule extraction is performed to support the
analysis of uncertain predictions. Using this method, ensembles can be
used for predictive modeling, in spite of their opacity and sometimes
insufficient global performance, while the involvement of a decision
maker is minimized.
The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other two
are improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.
Place, publisher, year, edition, pages
rule extraction, genetic programming, uncertainty estimation, machine learning, artificial neural networks, information fusion, Computer Science
Computer and Information Science Information Systems
IdentifiersURN: urn:nbn:se:hb:diva-3517Local ID: 2320/5134OAI: oai:DiVA.org:hb-3517DiVA: diva2:876907
This work was supported by the Information Fusion Research
Program (www.infofusion.se) at the University of Skövde, Sweden, in
partnership with the Swedish Knowledge Foundation under grant