Expert Systems with Applications: An International Journal
On Three Types of Covering-Based Rough Sets
IEEE Transactions on Knowledge and Data Engineering
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Artificial neural network and wavelet neural network approaches for modelling of a solar air heater
Expert Systems with Applications: An International Journal
Emergent rough set data analysis
Transactions on Rough Sets II
Data stream classification with artificial endocrine system
Applied Intelligence
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing with traditional rough set and neural networks approaches.