Data mining using extensions of the rough set model
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Knowledge acquisition from quantitative data using the rough-set theory
Intelligent Data Analysis
Discovering patterns of missing data in survey databases: An application of rough sets
Expert Systems with Applications: An International Journal
A hybrid approach to design efficient learning classifiers
Computers & Mathematics with Applications
Exploring high-performers' required competencies
Expert Systems with Applications: An International Journal
Hierarchical decision rules mining
Expert Systems with Applications: An International Journal
An application of fuzzy information granulation in the emerging area of online sports
Expert Systems with Applications: An International Journal
Hierarchical information system and its properties
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Anonymizing classification data using rough set theory
Knowledge-Based Systems
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
Hi-index | 12.06 |
Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. Recently, the rough-set theory has been widely used in dealing with data classification problems. Most of the previous studies on rough sets focused on deriving certain rules and possible rules on the single concept level. Data with hierarchical attribute values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values. It is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data. Boundary approximations, instead of upper approximations, are used to find possible rules, thus reducing some subsumption checking. Some pruning heuristics are also adopted in the proposed algorithm to avoid unnecessary search.