Fault diagnosis using Rough Sets Theory
Computers in Industry
Fuzzy discretization of feature space for a rough set classifier
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Intra-pulse modulation recognition of unknown radar emitter signals using support vector clustering
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A hybrid classifier based on rough set theory and support vector machines
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Computer Methods and Programs in Biomedicine
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
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Existing discretization methods cannot process continuous interval-valued attributes in rough set theory. This paper extended the existing definition of discretization based on cut-splitting and gave the definition of generalized discretization using class-separability criterion function firstly. Then, a new approach was proposed to discretize continuous interval-valued attributes. The introduced approach emphasized on the class-separability in the process of discretization of continuous attributes, so the approach helped to simplify the classifier design and to enhance accurate recognition rate in pattern recognition and machine learning. In the simulation experiment, the decision table was composed of 8 features and 10 radar emitter signals, and the results obtained from discretization of continuous interval-valued attributes, reduction of attributes and automatic recognition of 10 radar emitter signals show that the reduced attribute set achieves higher accurate recognition rate than the original attribute set, which verifies that the introduced approach is valid and feasible.