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Dynamic Programming Approach for Construction of Association Rule Systems

Alsolami F., Amin T., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2016

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Vol. 147, nr 2/3

159--171

EN

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

2

Relationships Between Length and Coverage of Decision Rules

Amin T., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2014

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Vol. 129, nr 1/2

1--13

EN

The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

3

Classifiers Based on Optimal Decision Rules

Amin T., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

151--160

EN

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification – exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

4

Dynamic Programming Approach for Partial Decision Rule Optimization

Amin T., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2012

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Vol. 119, nr 3/4

233-248

EN

This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number , we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most . The graph Δ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.