Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms

Sikora, Marek

doi:10.1007/978-3-642-11479-3_8

Cited by 27 publications

(36 citation statements)

References 36 publications

(53 reference statements)

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“…The quality measure applied in decision rule induction is significant for the quality of an output set of rules. This is confirmed by empirical research [32,20,21,23,25,26].…”

Section: Related Worksupporting

confidence: 60%

“…If the analysis objective is to create a classification system that uses an interpretable data model, application of sequential covering rule induction algorithms is the most sensible solution. The quality of the rule set obtained by the covering algorithm depends on the quality measure [20,1,21,22,23,24,25,26] used in the growing and pruning phases. The used quality measure is one of the factors affecting the classification accuracy, the number of rules induced and their other characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Michalak

Sikora

Wrobel³

2015

Annals of Computer Science and Information Systems

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Abstract-The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only these quality measures (9 measures) which had been recognised as effective based on previously conducted research.

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“…The quality measure applied in decision rule induction is significant for the quality of an output set of rules. This is confirmed by empirical research [32,20,21,23,25,26].…”

Section: Related Worksupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Michalak

Sikora

Wrobel³

2015

Annals of Computer Science and Information Systems

Self Cite

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“…Wisconsin Breast Cancer data set is not such popular, but our results can be compared to approaches (92-99%) presented and referenced in [10] for different rule set sizes. Although the efficiency results for the Heart Disease and Appendicitis data sets are not enough satisfactory, they are better or competitive to approaches presented and referenced in [19,25].…”

Section: Discussionmentioning

confidence: 85%

Membership Functions for Fuzzy Focal Elements

Porebski

Straszecka

2016

Archives of Control Sciences

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The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

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“…RSES V2 presents the rules in the following format: RSES V2 p rovides the consumer with an option to shorten the generated rules, in addition to the basic functionalities presented in the theory of Rough Sets. Rule shortening and generalization has been investigated by researchers [43][44][45]. In RSES, ru le shortening is an advanced process that tries to condense the descriptor provided along with the premise of the rules [46].…”

Section: A Rough Sets Toolmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

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Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

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Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms

Cited by 27 publications

References 36 publications

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Rule Quality Measures Settings in a Sequential Covering Rule Induction Algorithm - an Empirical Approach

Membership Functions for Fuzzy Focal Elements

A Rough Sets-based Agent Trust Management Framework

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