Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variable precision rough set model
Journal of Computer and System Sciences
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning
Machine Learning
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combining neural networks and semantic feature space for email classification
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
Determination of the threshold value β of variable precision rough set by fuzzy algorithms
International Journal of Approximate Reasoning
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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This paper proposed a new hybrid method, designated as PSOVPRS-index method, for partitioning and classifying continuous valued datasets based on particle swarm optimization (PSO) algorithm, Variable Precision Rough Set (VPRS) theory and a modified form of the Huang-index function. In contrast to the Huang-based index method which simply assigns a constant number of clusters to each attribute and in which the Rough Set (RS) theory is applied, this method could not only cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy, but also extends the applicability of classification using VPRS theory. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset containing stock market information with those obtained by PSORS-index method and pseudo-supervised decision-tree classification method. There is good evidence to show that the proposed PSOVPRS-index method not only has a better classification performance than the considered methods, but also achieves a more reliable basis for the extraction of decision-making rules.