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Rough set-based dimensionality reduction for supervised and unsupervised learning

100%

Shen Q., Chouchoulas A.

International Journal of Applied Mathematics and Computer Science

2001

tom 11

nr 3

583-601

The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality of datasets as a preprocessing step to training a learning system on the data. This paper investigates the utility of the Rough Set Attribute Reduction (RSAR) technique to both supervised and unsupervised learning in an effort to probe RSAR's generality. FuREAP, a Fuzzy-Rough Estimator of Algae Populations, which is an existing integration of RSAR and a fuzzy Rule Induction Algorithm (RIA), is used as an example of a supervised learning system with dimensionality reduction capabilities. A similar framework integrating the Multivariate Adaptive Regression Splines (MARS) approach and RSAR is taken to represent unsupervised learning systems. The paper describes the three techniques in question, discusses how RSAR can be employed with a supervised or an unsupervised system, and uses experimental results to draw conclusions on the relative success of the two integration efforts.

Engineering intelligent systems on the knowledge formalization continuum

100%

Baumeister J., Reutelshoefer J., Puppe F.

International Journal of Applied Mathematics and Computer Science

2011

tom 21

nr 1

27-39

In spite of their industrial success, the development of intelligent systems is still a complex and risky task. When building intelligent systems, we see that domain knowledge is often present at different levels of formalization-ranging from text documents to explicit rules. In this paper, we describe the knowledge formalization continuum as a metaphor to help domain specialists during the knowledge acquisition phase. To make use of the knowledge formalization continuum, the agile use of knowledge representations within a knowledge engineering project is proposed, as well as transitions between the different representations, when required. We show that a semantic wiki is a flexible tool for engineering knowledge on the knowledge formalization continuum. Case studies are taken from one industrial and one academic project, and they illustrate the applicability and benefits of semantic wikis in combination with the knowledge formalization continuum.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

1 Baumeister J.

1 Chouchoulas A.

1 Puppe F.

1 Reutelshoefer J.

1 Shen Q.

1 2011

1 2001

Wyniki wyszukiwania

Rough set-based dimensionality reduction for supervised and unsupervised learning

Engineering intelligent systems on the knowledge formalization continuum