Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Utilizing hierarchical feature domain values for prediction
Data & Knowledge Engineering
Learning cross-level certain and possible rules by rough sets
Expert Systems with Applications: An International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Rough sets approach to symbolic value partition
International Journal of Approximate Reasoning
Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Fuzzy rough sets with hierarchical quantitative attributes
Expert Systems with Applications: An International Journal
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
Information Sciences: an International Journal
Hierarchical decision rules mining
Expert Systems with Applications: An International Journal
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
Building a Rule-Based Classifier—A Fuzzy-Rough Set Approach
IEEE Transactions on Knowledge and Data Engineering
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Rough set based approaches to feature selection for Case-Based Reasoning classifiers
Pattern Recognition Letters
Feature subset selection wrapper based on mutual information and rough sets
Expert Systems with Applications: An International Journal
From data to global generalized knowledge
Decision Support Systems
An efficient rough feature selection algorithm with a multi-granulation view
International Journal of Approximate Reasoning
Attribute reduction of data with error ranges and test costs
Information Sciences: an International Journal
Attribute reduction for dynamic data sets
Applied Soft Computing
Attribute selection based on a new conditional entropy for incomplete decision systems
Knowledge-Based Systems
Anonymizing classification data using rough set theory
Knowledge-Based Systems
Feature selection with test cost constraint
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Attribute reduction and attribute generalization are two basic methods for simple representations of knowledge. Attribute reduction can only reduce the number of attributes and is thus unsuitable for attributes with hierarchical domains. Attribute generalization can transform raw attribute domains into a coarser granularity by exploiting attribute value taxonomies (AVTs). As the control of how high an attribute should be generalized is typically quite subjective, it can easily result in over-generalization or under-generalization. This paper investigates knowledge reduction for decision tables with AVTs, which can objectively control the generalization process, and construct a reduced data set with fewer attributes and smaller attribute domains. Specifically, we make use of Shannon's conditional entropy for measuring classification capability for generalization and propose a novel concept for knowledge reduction, designated attribute-generalization reduct, which can objectively generalize attributes to maximize high levels while keep the same classification capability as the raw data. We analyze major relationships between attribute reduct and attribute-generalization reduct and prove that finding a minimal attribute-generalization reduct is an NP-hard problem and develop a heuristic algorithm for attribute-generalization reduction, namely, AGR-SCE. Empirical studies demonstrate that our algorithm accomplishes better classification performance and assists in computing smaller rule sets with better generalized knowledge compared with the attribute reduction method.