Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
IEEE Transactions on Fuzzy Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper employs pattern classification methods for assisting investors in making financial decisions. Specifically, the problem entails the categorization of investment recommendations. Based on the forecasted performance of certain indices, the Stock Quantity Selection Component is to recommend to the investor to purchase stocks, hold the current investment position or sell stocks in possession. Three designs of the component were implemented and compared in terms of their complexity as well as scalability. Designs that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using Artificial Neural Networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern.