A Hybrid Higher Order Neural Classifier for handling classification problems

In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of different high-order units and updating the learning parameters. Structure, model selection and updating the learning parameters of HHONC is introduced and is applied in classification of the Iris data set, the breast cancer data set, the Wine recognition data set, the Glass identification data set, the Balance scale data set, and the Pima diabetes data set. Acquired results are compared with the methods presented in Chen and Shie (2009). It is observed that the fewer features the dataset contains, the more accurate the HHONC performs, however the accuracy of datasets with more features are acceptable. Experimental results show about 3.5% and 0.6% improvements compared to the best accuracy obtained in previously methods for classifying the Pima diabetes and Iris datasets, respectively. In addition, by using a same method for reducing the feature number, it’s shown the proposed method perform more accurate than methods presented in Shie and Chen (2008). In this case, improvements compared to the best acquired accuracy of mentioned methods are about 1.7%, 1.3% and 0.2% in classification of Pima, Iris and Breast cancer datasets, respectively.