Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Computational methods for rough classification and discovery
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
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 set approach to incomplete information systems
Information Sciences: an International Journal
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On Application of Rough Data Mining Methods to Automatic Construction of Student Models
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Decision Making with Probabilistic Decision Tables
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Classification with diffuse or incomplete information
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Classification with diffuse or incomplete information
WSEAS Transactions on Systems and Control
A rough set approach for automatic key attributes identification of zero-day polymorphic worms
Expert Systems with Applications: An International Journal
Discovering patterns of missing data in survey databases: An application of rough sets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Extraction of the Reduced Training Set Based on Rough Set in SVMs
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Extended Pawlak's Flow Graphs and Information Theory
Transactions on Computational Science V
Analysis of classification in interval-valued information systems
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Exploring high-performers' required competencies
Expert Systems with Applications: An International Journal
Fuzzy Sets and Systems
Applying variable precision rough set model for clustering student suffering study's anxiety
Expert Systems with Applications: An International Journal
Topological characterizations of covering for special covering-based upper approximation operators
Information Sciences: an International Journal
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
International Journal of Software Science and Computational Intelligence
Hi-index | 12.06 |
Induction of classification rules based on rough set theory has been an active research area in the field of machine learning. However, pure rough set theory is not well suited for analyzing noisy information systems. This paper adopts a generalization of rough set model based on fuzzy lower approximation with respect to information granules. Based on the fuzzy lower approximation, a concept of tolerant approximation is introduced to deal with the problem of discovering effective rules from noisy data. An efficient rule induction algorithm based on the tolerant lower approximation is proposed, and two heuristics are investigated to study their inductive effectiveness. Empirical experiments are conducted on five real-life data sets, acknowledged in the machine learning community, using the algorithms. The Tree classification algorithm from the IBM Intelligent Miner is also investigated as a comparison basis. Effectiveness measurements include the prediction accuracy, cost ratio and the rule validation rate based on randomization analysis. The empirical evidences show that the proposed algorithm is effective in dealing with rule induction in noisy environments.