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Fuzzy Granular Hyperplane Classifiers
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DOI：10.1109/ACCESS.2020.3002904.png)
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Abstract
En 中文
Granular computing has advantage of knowledge discovery for complex data. In the paper, we present Fuzzy Granular Hyperplane Classifiers (FGHCs) for data classification from a new angle of Granular Computing. First, we introduce a fuzzy granular hyperplane concept by defining fuzzy granule, fuzzy granular vector, metrics and operators. Next, for binary classification problem, we present solving optimal fuzzy granular hyperplane through evolution strategy; the learning algorithm of parameters and the prediction algorithm of instances are also proposed. Finally, a multi-classification prediction model is designed by combining a set of Fuzzy Granular Hyperplane Classifiers based on vote strategy. In order to evaluate performance, we employed 10-fold cross validation to verify on UCI dataset and Alzheimer's Disease Voice dataset. Theoretical analysis and experiments demonstrated that FGHCs have good performance.
Keywords:
Support vector machines
Prediction algorithms
Machine learning
Training
Licenses
Computational modeling
Big Data
Fuzzy granular hyperplane
machine learning
granular computing
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