Add to ORKGIt is one of the key problems for web based decision support systems to generate knowledge from huge database containing inconsistent information. In this paper, a learning algorithm for multiple rule trees (MRT) is developed, which is based on ID3 algorithm and rough set theory. MRT algorithm can quickly generate decision rules from inconsistent decision information tables. Both space and time complexities of MRT algorithm are just polynomial, while those of Skowron’s default decision rule generation algorithm are exponential. With the increasing of the number of records and core attributes of an information table, Skowron’s default algorithm needs more memory and time for generating rules than MRT algorithm. In some cases, Skowron’s defau...
This paper introduces DEFT, a new multitask learning approach for rule learning algorithms. Like oth...
Abstract. This paper introduces Deft, a new multitask learning approach for rule learning algorithms...
We focus on developing improvements to algorithms that generate decision trees from training data. T...
In rough set theory, the reduct is defined as a minimal set of attributes that partitions the tuple ...
[[abstract]]The rough set (RS) theory can be seen as a new mathematical approach to vagueness and is...
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they ...
Part 6: Decision AlgorithmsInternational audienceIn rough set theory, not too much work pays attenti...
In this paper it is considered rule reduct generation problem, based on Rough Set Theory. Rule Reduc...
Abstract—Traditional rough set-based approaches to reduct have difficulties in constructing optimal ...
Based on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an increment...
Analyzes the traditional methods of extracting decision rules in Rough Sets, defines the concept of ...
Database stores a huge amount of information in a structured and organized manner and provides many ...
We introduce a novel algorithm for decision tree learning in the multi-instance setting as originall...
AbstractIn this paper, we present three approaches for construction of decision rules for decision t...
AbstractAn incremental algorithm generating satisfactory decision rules and a rule post-processing t...
This paper introduces DEFT, a new multitask learning approach for rule learning algorithms. Like oth...
Abstract. This paper introduces Deft, a new multitask learning approach for rule learning algorithms...
We focus on developing improvements to algorithms that generate decision trees from training data. T...
In rough set theory, the reduct is defined as a minimal set of attributes that partitions the tuple ...
[[abstract]]The rough set (RS) theory can be seen as a new mathematical approach to vagueness and is...
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they ...
Part 6: Decision AlgorithmsInternational audienceIn rough set theory, not too much work pays attenti...
In this paper it is considered rule reduct generation problem, based on Rough Set Theory. Rule Reduc...
Abstract—Traditional rough set-based approaches to reduct have difficulties in constructing optimal ...
Based on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an increment...
Analyzes the traditional methods of extracting decision rules in Rough Sets, defines the concept of ...
Database stores a huge amount of information in a structured and organized manner and provides many ...
We introduce a novel algorithm for decision tree learning in the multi-instance setting as originall...
AbstractIn this paper, we present three approaches for construction of decision rules for decision t...
AbstractAn incremental algorithm generating satisfactory decision rules and a rule post-processing t...
This paper introduces DEFT, a new multitask learning approach for rule learning algorithms. Like oth...
Abstract. This paper introduces Deft, a new multitask learning approach for rule learning algorithms...
We focus on developing improvements to algorithms that generate decision trees from training data. T...