A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Finding useful fuzzy concepts for pattern classification using genetic algorithm
Information Sciences: an International Journal
Oriya Handwritten Numeral Recognition Syste
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
Pattern Recognition Letters
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Linear dimensionality reduction using relevance weighted LDA
Pattern Recognition
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Hierarchical Bayesian network for handwritten digit recognition
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Hierarchical Bayesian network for handwritten digit recognition
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Analytic network process for pattern classification problems using genetic algorithms
Information Sciences: an International Journal
Classification using the variable precision rough set
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The Knowledge Engineering Review
Handwritten digit recognition using low rank approximation based competitive neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Recognition of off-line handwritten devnagari characters using quadratic classifier
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Using one axiom to characterize rough set and fuzzy rough set approximations
Information Sciences: an International Journal
Rough set approach to incomplete numerical data
Information Sciences: an International Journal
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This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity threshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that 1) some tolerant objects are required to be included in the same class as many as possible and 2) some objects in the same class are required to be tolerable as much as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grouped into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approximation at the first stage and then the nonclassified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification problem and compare its classification performance and learning time with those of the feedforward neural network's backpropagation algorithm.